# A catalogue of 323 cataclysmic variables from LAMOST DR6

Yongkang Sun111The first author College of Physics, Hebei Normal University,
Shijiazhuang 050024, China  Zhenghao Cheng111The first author College of
Physics, Hebei Normal University, Shijiazhuang 050024, China  Shuo Ye
Software College, Hebei Normal University, Shijiazhuang 050024, China  Ruobin
Ding  College of Physics, Hebei Normal University, Shijiazhuang 050024, China
Yijiang Peng  College of Physics, Hebei Normal University, Shijiazhuang
050024, China  Jiawen Zhang  College of Physics, Hebei Normal University,
Shijiazhuang 050024, China  Zhenyan Huo  College of Physics, Hebei Normal
University, Shijiazhuang 050024, China  Wenyuan Cui College of Physics, Hebei
Normal University, Shijiazhuang 050024, China  Xiaofeng Wang  Physics
Department, Tsinghua University, Beijing 100084, China  Beijing Planetarium,
Beijing Academy of Sciences and Technology, Beijing 100044, China  Jianrong
Shi  School of Astronomy and Space Science, University of Chinese Academy of
Sciences, Beijing 100049, China  Key Laboratory of Optical Astronomy, National
Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012, China
Jie Lin  Physics Department, Tsinghua University, Beijing 100084, China
Chengyuan Wu  Physics Department, Tsinghua University, Beijing 100084, China
Linlin Li  College of Physics, Hebei Normal University, Shijiazhuang 050024,
China  Shuai Feng  College of Physics, Hebei Normal University, Shijiazhuang
050024, China  Yang Yu  College of Physics, Hebei Normal University,
Shijiazhuang 050024, China  Xiaoran Ma  Physics Department, Tsinghua
University, Beijing 100084, China  Xin Li  Beijing Planetarium, Beijing
Academy of Sciences and Technology, Beijing 100044, China  Cheng Liu  Beijing
Planetarium, Beijing Academy of Sciences and Technology, Beijing 100044, China
Ziping Zhang  Beijing Planetarium, Beijing Academy of Sciences and Technology,
Beijing 100044, China  Zhenzhen Shao  Beijing Planetarium, Beijing Academy of
Sciences and Technology, Beijing 100044, China

###### Abstract

In this work, we present a catalogue of cataclysmic variables (CVs) identified
from the Sixth Data Release (DR6) of the Large Sky Area Multi-Object Fiber
Spectroscopic Telescope (LAMOST). To single out the CV spectra, we introduce a
novel machine-learning algorithm called UMAP to screen out a total of 169,509
H$\alpha$-emission spectra, and obtain a classification accuracy of the
algorithm of over 99.6$\%$ from the cross-validation set. We then apply the
template matching program PyHammer v2.0 to the LAMOST spectra to obtain the
optimal spectral type with metallicity, which help us identify the
chromospherically active stars and potential binary stars from the 169,509
spectra. After visually inspecting all the spectra, we identify 323 CV
candidates from the LAMOST database, among them 52 objects are new. We further
discuss the new CV candidates in subtypes based on their spectral features,
including five DN subtype during outbursts, five NL subtype and four magnetic
CVs (three AM Her type and one IP type). We also find two CVs that have been
previously identified by photometry, and confirm their previous classification
by the LAMOST spectra.

cataclysmic variable stars, catalogues, spectral classification

††journal: ApJS

##  1 Introduction

Cataclysmic variable stars (CVs) are semi-detached binary systems with short
orbital periods ranging from shorter than one hour to longer than six hours
(Warner, 2003; Zorotovic et al., 2016). They typically consist of a white
dwarf star and a late-type main-sequence companion. With the loss of angular
momentum, the mass transfer is continuously or intermittently activated
through the inner Lagrangian point of the Roche lobe, usually leading to the
formation of an accretion disc.

The CVs can be divided into several types: classical nova (CN), recurrent nova
(RN), dwarf nova (DN), nova-like variables (NL), polars (or AM Her type), and
intermediate polars (IPs, including DQ Her type) (Warner, 2003). The latter
two are magnetic CVs. Note that in polars, the magnetic field is typically
stronger than 20 MG that an accretion disc can not form. Classical novae
usually have only one observed eruption by definition. If a classical nova is
observed with a second eruption, it will be classified as a recurrent nova.
Dwarf novae usually have frequent and regular bursts as a result of
instability of the accretion disk (Morales-Rueda & Marsh, 2002). U Gem stars
are the prototype of DN subtype of CVs which have repeated normal outbursts.
The SU UMa subtype of DNs presents superhumps in addition to normal outbursts
and the Z Cam subtype of DNs are characteristic of standstills in the light
curve (Szkody, 1987; Hellier, 2001). Nova-like objects (NLs) are recognized as
novae between eruptions and do not have a recorded outburst. Some NLs have
been observed to occasionally change from a high state with higher brightness
to a low state with lower brightness (La Dous, 1994; King & Cannizzo, 1998;
Rodríguez-Gil et al., 2015). In the AM Her systems, the magnetic field of the
white dwarf is strong enough for both stars to be locked into the same
rotation period. In this system, the mass flow is controlled by the magnetic
field and falls directly toward the surface of the white dwarf.
Correspondingly, the accretion disk of IPs is truncated at the radius of the
magnetosphere. Sometimes these two types of magnetic CVs are referred to
jointly as one subtype of nova-like variables (Coppejans et al., 2015).

As a special kind of interacting binary systems, CVs have complex physical
processes and unique observation properties in various wave bands and show
luminosity variations of different time scales and amplitudes. They are
important samples that help us to understand the evolution of binary stars and
even the explosion mechanism of type Ia supernovae (SNe Ia) (Hachisu & Kato,
2001; Anupama, 2011). The acquisition and analysis of more CV samples are
helpful to the refining of the theoretical models of CVs.

So far, numerous works have been conducted to search for CVs. Downes et al.
(2001) published a catalogue including spectra, which is a revision and
supplement to the first two editions (Edition 1 - Downes & Shara (1993) and
Edition 2 - Downes et al. (1997)). This catalogue consists of 1034 CVs and 194
non-CV objects. Using the Sloan Digital Sky Survey (SDSS) I/II, Szkody et al.
(2011) published a summary table of 285 CVs with the SDSS spectra. Jiang et
al. (2013) identified CVs from the low-resolution spectra of LAMOST by using
the support vector machine technique (SVM) combined with principal component
analysis (PCA). They identified 10 cataclysmic variables, of which two are new
CV candidates. Breedt et al. (2014) provided spectroscopic identification of
85 systems fainter than g$\geq$19 mag and analyzed the photometric properties
of the 1043 CV candidates from the Catalina Real-time Transient Survey (CRTS).
Coppejans et al. (2016) published an outburst catalogue which contains a wide
variety of observational properties for 722 dwarf nova-type CVs and 309 CVs of
other types from the CRTS. By cross-matching the published catalogues with
LAMOST DR3, Han et al. (2018) collected 48 known CVs, and they found three new
CVs using the method adopted by Jiang et al. (2013). Szkody et al. (2020)
published 329 objects as known or candidate CVs during the first year of
testing and operation of the Zwicky Transient Facility. Hou et al. (2020) used
the Bagging and the Random Forest algorithms to search for CVs from LAMOST
DR5, and they identified 245 cataclysmic variables, of which 52 CV candidates
are new.

In this work, we present a catalogue of 323 CV candidates identified from
LAMOST DR6, of which 52 objects are new. The structure of this paper is as
follows. Section 2 describes the method and process to identify the CV
candidates according to the spectral features. Section 3 presents the results.
Section 4 is the discussion of the spectral properties and subtype
classification of the LAMOST CV candidates. Section 5 is the summary.

##  2 Data

The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) is a
4-meter quasi-meridian reflecting Schmidt telescope (Zhao et al., 2012; Cui et
al., 2012) that is operated by the National Astronomical Observatories,
Chinese Academy of Sciences. With a field of view of 20 deg2 and 4000 fibers
at its focal plane, LAMOST can obtain up to 4000 spectra in one exposure and
it has been the most efficient telescope for spectral acquisition in the
world. Up to July 2018, LAMOST has completed its pilot survey and the first
six years of regular survey which was initiated on September 2012. After the
six-year-survey, the whole data (DR6) were released in 2018, containing
9,911,337 low-resolution spectra. They are flux(relatively)- and wavelength-
calibrated, sky-subtracted and have a resolution of R $\sim$ 1800 at 5500Å and
a wavelength coverage of 3700Å $\leq$ $\lambda$ $\leq$ 9000Å. Each spectrum is
spliced by the blue part and the red part. The integration time is 600, 1500
or 1800 seconds. All the low-resolution spectra were generally classified into
different types through the pipeline including 9,231,057 stellar spectra,
177,270 galaxy spectra, 62,168 quasar spectra, and 440,842 spectra marked as
unknown. The massive spectral database obtained by LAMOST is a rich resource
for us to identify different kinds of peculiar stars. As CVs are one kind of
valuable objects, searching them from the massive database is of much
importance. And a larger CV catalogue is greatly helpful for follow-up
studies.

##  3 Method

We first introduce a machine learning algorithm called UMAP for screening out
the spectra with prominent H$\alpha$ emission features, which leads to a total
of 169,509 spectra. The sample data used for supervised machine learning comes
from 10 published CV or X-ray binary catalogues which are: Downes & Shara
(1993); Downes et al. (1997); Downes et al. (2001); Szkody et al. (2011);
Jiang et al. (2013); Breedt et al. (2014); Coppejans et al. (2016); Han et al.
(2018); Ritter & Kolb (2003); Hou et al. (2020). The PyHammer v2.0 program is
introduced to fit the LAMOST low-resolution spectra to help the manual
inspection. There are two rounds in our manual inspection. In the first round,
those spectra of late-type main-sequence stars with enhanced chromospheric
activities are removed. In the second round, we identify the early-type
emission-line stars, pre-main-sequence, and other types of non-CV emission-
line objects. For the left, we finally picked out the potential CV candidates.

###  3.1 H$\alpha$ classification based on machine learning

As the H$\alpha$ is common to be an emission line for CV spectra, we first
screen out the spectra with H$\alpha$ emission features from the whole LAMOST
DR6 database.

####  3.1.1 Training sample

To obtain initial samples for supervised machine learning, we first cross-
match the above mentioned 10 published catalogues with the LAMOST DR6
database. We manually examine each of the spectra in the cross-matching result
and remove those that do not show H$\alpha$ emission features. Thus, we
obtaine 392 specrta with H$\alpha$ emission features. Note that not all these
spectra are actually CVs, as long as they show H$\alpha$ emission features.
The wavelength range 6530-6600 Å of these spectra are extracted out as an
input to the machine learning program (see Figure 1.) In order to avoid loss
of CVs during outbursts, those spectra that have H$\alpha$ wide absorption
with emission cores are included in the sample. The double-peaked H$\alpha$
profile is also included in the sample.

Then we randomly select 1000 spectra in the whole LAMOST DR6 database. We
manually check the H$\alpha$ line features, retaining those without typical
H$\alpha$ emission features, and obtain 973 spectra that can be used as
controls in the supervised machine learning.

Figure 1: These are 16 examples of 392 sample spectra that are used for
machine-learning training. The wavelength segment 6530-6600 Å associated with
the H$\alpha$ emission line is extracted out and used to screen for the same
H$\alpha$ emission line features.

####  3.1.2 UMAP with k-NN algorithm

Uniform Manifold Approximation and Projection (UMAP) is a novel machine-
learning algorithm for dimension reduction proposed by McInnes et al. (2018),
which is based on the mathematical framework of Riemannian geometry and
algebraic topology. UMAP tends to preserve more of the global structure in
dimension reduction while many other algorithms like PCA (Principal Components
Analysis) seek to preserve the distance structure within the data. UMAP is
also capable of preserving the local fine structure while still retaining much
of the large-scale global structure like how PCA does. Thus, UMAP has natural
advantages for dimension reduction of nonlinear data. Through our tests, the
PCA algorithm often has the problem that the classification boundary in
parameter space is not clear in comparison with the result of the UMAP
algorithm, and may cause large contamination. Moreover, UMAP also shows
superior run time performance for high-dimensional massive data compared to
PCA. We therefore select UMAP as the dimension reduction algorithm for the
high-dimensional spectral data. K-Nearest Neighbour (k-NN) is a common machine
learning classification algorithm. The k-NN classifier is utilized to classify
the data after the dimension reduction.

After training with the selected samples and testing several groups of
parameters in the UMAP with k-NN algorithm, 392 sample spectra with H$\alpha$
emission features and 973 without H$\alpha$ emission features can be clearly
separated in the three-dimensional space, as shown in Figure 2. The same
algorithm is applied to all spectra in the LAMOST DR6 database, and we obtain
a total of 169,509 H$\alpha$-emission spectra. After we conduct a five-fold
cross-validation (Shao, 1993), we obtain an classification accuracy of over
99.6$\%$ in the cross-validation set.

Figure 2: After dimension reduction by UMAP to 3 dimensions, 392
H$\alpha$-emission spectra and 973 randomly selected spectra without H$\alpha$
emission can be separated clearly.

###  3.2 PyHammer v2.0

The PyHammer v2.0 program was published by Roulston et al. (2020) to give a
more accurate classification of stellar spectra. We introduce this program to
obtain the optimal fitted spectra as a reference for our manual inspection. We
have modified the code to fit the LAMOST low-resolution spectra while still
use the original templates. In the first round of manual inspection, those
spectra that can be fitted well to the G/K/M-type templates considered as
normal main-sequence stars with enhanced chromospheric activities, are
removed. The spectra of this kind make up about half of the total 169,509
H$\alpha$-emission spectra. After removing the chromospherically active stars
from the first round, we go through the rest of the spectra to finally pick
out CV candidates. Figure 3 shows some examples of the spectral fitting using
PyHammer v2.0.

Figure 3: Examples of spectral fitting results obtained by using the Pyhammer
v2.0 program. We use such spectra to identify the continuum and screen out the
late type main sequence stars affected by chromosphere activity.

###  3.3 Manual inspection

####  3.3.1 CV features

Typical CV spectra show a high-temperature continuum superimposed with strong
emission lines, which are mainly Balmer and He I/He II lines. An important
feature is that the line width of Balmer and helium emission lines appear to
be significantly broad in most CV spectra, i.e. 70 Å for H$\alpha$. Thus the
existence of uniformly broad Balmer and He I/He II emission lines is a general
criterion to identify CVs. The CV spectra usually do not have a steep
decrement towards the higher order of the Balmer series as they can be seen up
to H8 or higher. The strongest Balmer line may usually be H$\alpha$ to
H$\delta$. The line width of the helium emission lines is usually close to
that of the Balmer lines. The double-peaked line profiles present in the
Balmer and He emission lines indicate the existence of a rotating accretion
disk. According to Knigge et al. (2000), when the disk is self-occulted, the
double-peaked emission lines tend to be narrower and straighter, which may be
the reason of the straight double-peaked profiles in some CV spectra. In some
cases, the Ca II H/K and Ca II triple lines also show broad emission, and the
superposition of Ca II H and H$\epsilon$ lines make H$\epsilon$ appear
stronger (close to or beyond H$\delta$) than it originally is. Moreover, when
a spectrum is dominated by the companion’s contribution (often a M-type
spectrum), the blue continuum would show an excess compared to the single
M-type star. However, it is possible that at certain orbital phases the
emission lines are obscured due to the eclipses, so that the spectrum does not
show any CV feature. For very rare cases such as AM CVn systems, they do not
have hydrogen lines due to the hydrogen-deficient nature.

For those spectra observed during outbursts, the Balmer lines show absorption
through optically thick materials. The central emission cores can be absent at
outburst peak and become larger if the spectrum is slightly past outburst. The
central emission cores often present in all visible Balmer series, and the
H$\alpha$ emission core is usually strong enough to maintain higher than the
continuum (this can be called a core emission). The He I lines are also
affected by wide absorption, while in many cases He II $\lambda$4686 is still
an emission line. During outbursts, He I $\lambda$6678 behaves similarly to
H$\alpha$ which usually shows emission and higher than the continuum. In some
rare cases, the absorption lines are very strong, which cause the emission
core of He I $\lambda$6678 to be lower than the continuum.

Although in some cases the Balmer emission lines can be quite narrow,
asymmetric broadening components can be found at the line wings that meet our
empirical criteria of 24 Å, thus the spectra can be classified as CV
candidates. Some of these cases belong to the AM Her-type CVs, for which the
TiO absorption bands from the companion can be identified in low states. As
mentioned in Warner (2003), the spectral lines in AM Her-type CVs can display
a combination of a broad ‘base’ component and narrow components due to a
combination of contributions of emission in the stream and pole regions. We
find that the known AM Her-type CVs can be identified by the complex line
structures and the profile differences between Balmer lines. The emission
lines of AM Her-type CVs are more complicated than those of IPs. Strong He II
$\lambda$4686 lines are common in NLs and magnetic CVs. For AM Her-type CVs,
He II $\lambda$4686 are often stronger than or comparable to H$\beta$.
However, in the spectra of some known AM Her-type CVs, He II lines including
He II $\lambda$4686 are very weak or absent. Accordingly, He II lines are
significant in the spectra of the known IPs (the strength ratio of He II
$\lambda$4686/H$\beta$ $\geq$ 0.5, or He II $\lambda$4686 is close to but
sightly weaker than H$\beta$). In NLs, the relative strength of C III/N III
$\lambda$4560 to He II $\lambda$4686 emission lines is generally greater than
that in magnetic CVs, and in some cases, C III/N III $\lambda$4560 emission
lines are as strong as He II $\lambda$4686\. Also, the emission lines of NLs
can be narrower during low states than in higher states (Rodríguez-Gil et al.,
2020). As C II emission lines appear commonly in NLs but not in DNs, the
presence of C II lines can be an important feature to distinguish between the
NL and the DN subclass.

More spectral line features of different subtypes of CV from the LAMOST
observations have been described in Hou et al. (2020). For the manual
inspection, the empirical criteria we chose to identify the potencial CVs are
listed below:

  * •

The apparent line width of H$\alpha$ is greater than or equal to 24 Å,

  * •

He I $\lambda$6678 emission line presents in the spectra and it is not too
narrow (less than 14 Å) compared to H$\alpha$; or other He emission lines
present,

  * •

Balmer series or He lines show double-peaked profiles,

  * •

Balmer series show wide absorption with emission cores and the emission cores
can be seen at the Balmer series higher than H$\gamma$.

Satisfying any of the above criteria will allow us to mark it out in the
manual inspection process. For low signal-to-noise ratio (SNR) spectra, when
limited Balmer series or only the H$\alpha$ emission line can be identified
visually, they will be included as a CV candidate.

####  3.3.2 Inspection process

Each of the 169,509 spectra obtained from the machine learning program has
been inspected manually in two rounds. In the first round, we remove the
chromospherically active main-sequence stars (which can be fitted well to the
G/K/M-type templates) by visually checking the whole spectra. In this process,
we compare the LAMOST spectra with the fitted template by PyHammer v2.0
program to check whether there is an excess at the blue end. The remaining
spectra, including those well-fitted to the O/B/A/F-type templates and binary
star templates, as well as those not well-fitted to the templates (many CVs
fall into this category), are retained for the second round. In the second
round, we visually check the detailed spectral features carefully and single
out those spectra that have CV features. Near the galactic plane, stellar
spectra may be affected by the H II regions. By examining the presence of He
absorption lines, we can remove the contaminations of the Oe/Be stars. By
judging the line width of H$\alpha$, we can remove the H II regions.

##  4 Results

Finally, we obtained 475 spectra for 323 objects in total from LAMOST DR6.
After checking the SIMBAD database, we find 271 known CVs or CV candidates,
and 52 objects are new.

###  4.1 Distribution

The spatial distribution of the identified CVs is shown in Figure 4. The upper
diagram is in equatorial coordinate, while the lower diagram is in galactic
coordinate. For the 224 objects indentified by Hou et al. (2020) from LAMOST
DR5, our results cover 215 of them, and nine of them are not recovered in our
results. In Section 4.2, we will specifically discuss why these nine objects
are not included in our results. From Figure 4, we can see that the known CVs
in LAMOST DR6 v2 are distributed evenly in space. The new CV candidates are
more abundant near the galactic plane; however, they are especially rare in
the northern galactic latitudes. Figure 5 shows the SNR distributions in $g$
and $i$ band for the LAMOST data. We can see that with our method, we have
found more CV candidates with lower SNRs than those of Hou et al. (2020) in
LAMOST DR6.

Figure 4: Spacial distributions of our results drawn in equatorial coordinates
and galactic coordinates respectively. The blue spots represent 271 known CV
objects from our analysis of LAMOST DR6 v2. The orange spots are 20 confirmed
new CV candidates in our results and the yellow spots are 32 possible new CV
candidates. The red cross markers indicate the 224 objects which are from
cross-matching the result of Hou et al. (2020) with LAMOST DR6 v2.  Figure 5:
SNR distributions of g and i band. The green bars represent all 475 spectra in
our result. The yellow bars represent the 220 spectra which are from cross-
matching the result of Hou et al. (2020) with LAMOST DR6 v2.

###  4.2 Cross-match with Hou et al. 2020

Hou et al. (2020) have presented 245 CV candidates from LAMOST DR5 and 224
objects are found in LAMOST DR6 v2 by cross-matching the coordinates. Out of
these 224 objects, we have 215 objects in common with Hou et al. (2020) and
nine objects are not included in our results (see Figure 6). We give the
reasons why we lost these nine objects.

The spectrum of J0117 is affected by the bad quality. The red part is flat and
does not have any feature, while the blue part shows an abnormally violent
change. Therefore, in our manual inspection, we consider this spectrum
unreliable and don’t include it in our results.

J1234 is the prototype star and is a representative of the AM CVn-type systems
with hydrogen-deficient. Due to the lack of H$\alpha$ emission, our results do
not include this type objects. The two spectra of this object do not show
visible hydrogen lines on the high-temperature continuum, and He I lines on
the blue part show broad absorption with partial filling at the center.

J0403 has two spectra in which the Balmer lines show deep absorption with
emission cores, and He I lines show weak emission cores. Because the H$\alpha$
emission core does not exceed the continuum, the UMAP algorithm failed to
screen it out, but it can be a CV candidate as the Balmer emission cores exist
up to H8 and He emission lines are marginally visible.

The spectrum of J1446 shows a component of a white dwarf on the blue part and
a M-type companion on the red part, and it is fitted to the M3+DA2.5 template
by the PyHammer program. Because its H$\alpha$ line shows an emission core
that does not exceed the continuum, the UMAP algorithm fails to identify it.

J1844 is a known dwarf nova, showing strong absorption with emission cores for
both Balmer and He I lines. Its H$\alpha$ line appears to have multiple
components, and the emission core at the center is below the continuum so the
UMAP algorithm fails to identify it.

The Balmer lines in the spectrum of J0043 show strong absorption. H$\beta$ and
higher members of Balmer series are partially filled up by emission
components, while the H$\alpha$ is fully filled in so the UMAP algorithm fails
to identify it as a CV. The He emission lines are barely visible.

J0331 is a known dwarf nova. The only spectrum shows very weak Balmer emission
lines and the He lines are almost invisible. As the spectrum can be fitted to
the M4 main-sequence template and does not show an excess on the blue end, we
cannot identify it as a CV from this spectrum.

One of the spectra of J0439 can be fitted with the M6 template. The narrow
Balmer emission lines may come from chromospheric activities. The other two
spectra of this object show an indistinctive excess on the blue part, and they
lack He lines, so we do not include the object in the manual inspection
process. But it can still be a possible CV candidate as it presents enhanced
Balmer emission lines and excess on the blue part compared with the template.

The spectrum of J0712 does not show an excess on the blue end relative to the
M5 template, so we do not include it in the manual inspection process. But the
enhanced Balmer emission and absorption center at higher Balmer lines still
make it a possible CV candidate.

In conclusion, constrained by our machine learning samples, the UMAP algorithm
has some limitations to perfectly classify the spectra that have strong wide
absorption in H$\alpha$ line with weak emission cores. If the spectra are
dominated by the main-sequence star and they don’t show an excess on the blue
part or any obvious emission lines, we can not identify it as a CV in the
manual inspection process. This phenomenon may be due to high inclination and
specific orbital phases, which is relatively rare for CVs. In addition, a few
spectra are affected by bad quality or very low SNRs. We considered them not
trustworthy in the manual inspection and hence do not include them.

###  4.3 Cross-match with other catalogues

We examine the cases which were previously classified as CV objects but are
not included in our results from analysis of the LAMOST spectra. We cross-
match the catalogs from the literature listed in section 3.1.1 with LAMOST
DR6. From the cross-matching result, a total of 230 objects are classified as
CVs or CV candidates in the SIMBAD database, in which 193 objects are found by
us. For the rest 37 objects, three objects show CV features in their LAMOST
spectra, and are discribed as follows. J1234, the prototype star of the
hydrogen-deficient AM CVn system, and the dwarf nova J1844 have been explained
in Section 4.2. J0713 (ASASSN-14mv) is also an AM CVn-type object (Denisenko
et al., 2014), which shows strong He I and He II emission lines but doesn’t
show hydrogen lines in its spectrum. The other 34 objects do not show features
that could be identified as CVs in their LAMOST spectra, though they have been
identified by spectroscopy or photometry. The likely reason is that they are
too faint or in a fainter state that LAMOST can not obtain relatively high SNR
spectra. Also, it is possible that the emission regions are obscured, or they
were incorrectly identified as CVs in the catalogue (e.g. 2MASS
J01095921+2801244 in Downes et al. (2001)).

###  4.4 New CV Candidates

In our result, 52 objects are newly identified as CV candidates. Among them,
20 objects that can be classified as CVs as they show broad Balmer and He
emission lines, are listed in Table 1. The other 32 objects, which can be
classified as possible CV candidates, are listed in Table 2, as they lack some
of the distinct features of CVs, or the spectra may be affected by abnormal
circumstances, or they have a lower SNR. The 21 spectra of 20 confirmed CVs
and 52 spectra of 32 possible CV candidates are plotted in Figures 10 and 11
in the Appendix, respectively.

###  4.5 Catalogue Description

The 20 new confirmed CVs are presented in Table 1, while the 32 new possible
CV candidates are shown in Table 2. In Table 3, we present the 271 previously
known ones. The first two columns of each table are the LAMOST obsid (a unique
spectra ID) and designation. The 16 spectra from LAMOST DR6 v1 which no longer
exist in LAMOST DR6 v2 are marked by an asterisk before their obsid number.
Column (3)-(5) are the MJD, right ascension and declination, respectively.
Columns (6)-(8) are the CV subtype identified by us, Hou et al. (2020), and
from SIMBAD, respectively. We mark all the new CV candidates with a single
asterisk and label the 52 spectra of 32 new possible CV candidates as ‘CV?’ in
column (6). If a source has more than one spectrum but those spectra do not
show CV characteristics, we leave the row blank in column (6). Columns (9) and
(10) are the median magnitude and the magnitude difference between the maximum
and minimum taken from the ZTF DR4 data, respectively. The ZTF data are
obtained by best cross-matching coordinates within 2 arcsec.

Figure 6: Spectra of nine objects from the catalogue of Hou et al. (2020) but
are not found in our result. The wavelength range is from 3700 Å to 9000 Å.
Table 1: New confirmed CVs from LAMOST DR6

obsid | designation | mjd | ra | dec | subtypea | subtypeb | subtypec | medianmagd | magdifferencee
---|---|---|---|---|---|---|---|---|---
| (LAMOST) |  |  |  | (this work) | (Hou2020) | (SIMBAD) | (ZTF) | (ZTF)
472302069 | J002356.46+424707.8 | 57687 | 5.985285 | 42.7855 | *DN_burst |  | none |  |
354712003 | J014841.06+363350.0 | 57281 | 27.17111 | 36.56389 | *DN_burst |  | none | 15.68463 | 0.52242
371007239 | J014841.06+363350.0 | 57309 | 27.17111 | 36.56389 | *DN_burst |  | none |  |
600008212 | J021633.89+251346.5 | 58067 | 34.14122 | 25.22961 | *MCV(polar) |  |  | 18.80127 | 1.86958
353714066 | J032034.51+203607.2 | 57278 | 50.14383 | 20.602 | *MCV(polar) |  | none |  |
587803184 | J032641.99+475344.6 | 58018 | 51.67497 | 47.89573 | *MCV(polar) |  |  | 18.19204 | 2.89637
605413195 | J034713.84+161108.2 | 58074 | 56.80769 | 16.18563 | *CV |  | none | 16.91274 | 4.93641
203507028 | J040901.83+323955.6 | 56660 | 62.25766 | 32.66547 | *NL |  |  | 14.66591 | 0.60534
631412182 | J045452.12+491709.2 | 58135 | 73.71717 | 49.28589 | *NL |  |  | 17.20238 | 5.18567
642708209 | J045745.94+312517.2 | 58156 | 74.44143 | 31.42146 | *NL |  |  | 17.7648 | 0.92789
593404034 | J051949.51+642921.6 | 58045 | 79.95633 | 64.48935 | *NL |  | none | 17.29306 | 1.69211
19714085 | J052346.16+295034.5 | 55917 | 80.94235 | 29.84292 | *CV |  |  | 17.48023 | 0.13482
198710203 | J053532.64+220006.8 | 56652 | 83.88601 | 22.0019 | *CV |  |  | 19.52661 | 1.4481
400115034 | J061249.27+150642.1 | 57391 | 93.20533 | 15.11172 | *DN_burst |  | none | 17.73853 | 0.33707
108714229 | J065237.19+243622.1 | 56309 | 103.155 | 24.60617 | *CV |  | none | 15.70214 | 1.40907
422415102 | J070133.82+161010.3 | 57427 | 105.3909 | 16.16954 | *DN_burst |  |  | 18.35857 | 4.36762
631513222 | J071553.88+581606.4 | 58135 | 108.9745 | 58.26846 | *MCV(IP) |  |  | 16.17882 | 1.2921
600414109 | J072304.77+020437.4 | 58067 | 110.7699 | 2.077058 | *NL |  | none | 15.48614 | 0.38905
587208191 | J202300.37+063142.4 | 58016 | 305.7516 | 6.528447 | *NL? |  | none |  |
593810181 | J211249.93+374225.8 | 58046 | 318.2081 | 37.70719 | *DN_burst |  |  | 16.50493 | 0.41542
173307033 | J225017.79+060645.8 | 56598 | 342.5741 | 6.112739 | *CV |  | none | 16.19793 | 0.38934

Note. —

a If a source has more than one spectrum but the spectrum has no visible CV
characteristics, we leave the row blank. If the row is the spectrum of a new
CV candidate, we mark it with a single asterisk in this column. For new
confirmed CV candidates, we particularly give the subtype inferred from the
spectrum. Two poorly known objects discussed in section 5.2 are marked by
double asterisks.

b The column is the CV subtype for the corresponding LAMOST DR6 spectra given
by Hou et al. (2020).

c The column is the CV subtype from SIMBAD, or from the literature subtype
given in the catalogue of Hou et al. (2020). If the source exists in SIMBAD
but is given a type other than CV, it is marked as ‘none’.

d This is the median magnitude taken from the ZTF DR4. The ZTF data is
obtained by best matching within 2 arcsec with our result.

e This is the difference between the maximum and minimum magnitude taken from
the ZTF DR4.

Table 2: New possible CV candidates from LAMOST DR6

obsid | designation | mjd | ra | dec | subtypea | subtypeb | subtypec | medianmagd | magdifferencee
---|---|---|---|---|---|---|---|---|---
| (LAMOST) |  |  |  | (this work) | (Hou2020) | (SIMBAD) | (ZTF) | (ZTF)
18508092 | J005651.51+375620.4 | 55914 | 14.21465 | 37.93903 | *CV? |  |  | 19.83481 | 0.52011
16115150 | J031108.31+530719.3 | 55910 | 47.78466 | 53.12206 | *CV? |  | none |  |
181509193 | J035603.23+351450.5 | 56617 | 59.01347 | 35.24738 | *CV? |  |  | 14.89156 | 1.63763
212414002 | J035603.23+351450.5 | 56684 | 59.01347 | 35.24738 | *CV? |  |  |  |
182009193 | J035603.23+351450.6 | 56617 | 59.01347 | 35.24741 | *CV? |  |  |  |
194714002 | J035603.23+351450.6 | 56645 | 59.01347 | 35.24741 |  |  |  |  |
194716052 | J035933.23+360323.1 | 56645 | 59.88847 | 36.05643 | *CV? |  |  | 14.9497 | 0.10124
181912155 | J035933.23+360323.1 | 56617 | 59.88847 | 36.05643 |  |  |  |  |
182012155 | J035933.23+360323.1 | 56617 | 59.88847 | 36.05643 |  |  |  |  |
194616052 | J035933.23+360323.1 | 56645 | 59.88847 | 36.05643 |  |  |  |  |
194816052 | J035933.23+360323.1 | 56645 | 59.88847 | 36.05643 |  |  |  |  |
17506020 | J041407.98+280120.3 | 55913 | 63.53326 | 28.02231 | *CV? |  |  | 15.74186 | 0.0913
119012144 | J041407.98+280120.3 | 56331 | 63.53326 | 28.02231 |  |  |  |  |
214612144 | J041407.98+280120.4 | 56687 | 63.53325 | 28.02234 |  |  |  |  |
214712144 | J041407.98+280120.4 | 56687 | 63.53325 | 28.02234 |  |  |  |  |
39906248 | J044644.51+400141.0 | 55967 | 71.68547 | 40.02806 | *CV? |  |  | 14.14061 | 0.05539
68812240 | J052443.05+350123.3 | 56219 | 81.17938 | 35.02314 | *CV? |  |  | 18.15303 | 1.49098
269314147 | J053416.07+342316.1 | 56981 | 83.567 | 34.38782 | *CV? |  |  |  |
132413240 | J053416.08+342316.1 | 56358 | 83.567 | 34.38781 | *CV? |  |  | 15.34793 | 0.1759
300813240 | J053416.08+342316.1 | 57042 | 83.567 | 34.38781 | *CV? |  |  |  |
16202089 | J054311.80+254445.8 | 55910 | 85.79917 | 25.74608 | *CV? |  |  | 14.88737 | 0.11771
81412213 | J054311.80+254445.8 | 56254 | 85.79917 | 25.74608 | *CV? |  |  | 14.88737 | 0.11771
406715055 | J054345.28+252601.0 | 57400 | 85.93869 | 25.43363 | *CV? |  |  | 17.47745 | 0.10737
108002166 | J054627.34+190011.9 | 56308 | 86.61396 | 19.00333 | *CV? |  |  | 13.49713 | 0.09616
81116232 | J055303.79+262659.4 | 56253 | 88.26582 | 26.44984 | *CV? |  |  | 20.81788 | 1.02977
124105039 | J055444.33+401846.5 | 56343 | 88.68471 | 40.31292 | *CV? |  |  | 14.74165 | 0.1076
83211001 | J055444.33+401846.5 | 56256 | 88.68471 | 40.31292 |  |  |  |  |
124405039 | J055455.84+401952.3 | 56343 | 88.73269 | 40.33121 | *CV? |  |  | 14.94291 | 0.19556
83111007 | J055455.84+401952.3 | 56256 | 88.73269 | 40.33121 |  |  |  |  |
116308162 | J055834.38+232004.9 | 56321 | 89.64327 | 23.33472 | *CV? |  |  | 15.45488 | 0.11825
116308163 | J055924.27+233707.6 | 56321 | 89.85116 | 23.61878 | *CV? |  |  | 14.19076 | 0.12284
81108248 | J055924.73+234341.1 | 56253 | 89.85307 | 23.72811 | *CV? |  |  | 19.08045 | 1.30034
11107016 | J060116.87+271327.1 | 55898 | 90.32029 | 27.22422 | *CV? |  |  | 18.20835 | 0.12946
127912024 | J060214.93+303536.8 | 56349 | 90.56224 | 30.59356 | *CV? |  |  | 14.32692 | 0.10813
485106105 | J062104.00+221554.7 | 57720 | 95.2667 | 22.26522 | *CV? |  |  |  |
486306105 | J062104.00+221554.7 | 57723 | 95.2667 | 22.26522 | *CV? |  |  |  |
486806105 | J062104.00+221554.7 | 57724 | 95.2667 | 22.26522 | *CV? |  |  |  |
497206105 | J062104.00+221554.7 | 57739 | 95.2667 | 22.26522 | *CV? |  |  |  |
506606105 | J062104.00+221554.7 | 57718 | 95.2667 | 22.26522 | *CV? |  |  |  |
508806105 | J062104.00+221554.7 | 57757 | 95.2667 | 22.26522 | *CV? |  |  |  |
509806105 | J062104.00+221554.7 | 57758 | 95.2667 | 22.26522 | *CV? |  |  |  |
510706105 | J062104.00+221554.7 | 57759 | 95.2667 | 22.26522 | *CV? |  |  |  |
603206105 | J062104.00+221554.7 | 58076 | 95.2667 | 22.26522 | *CV? |  |  |  |
606206105 | J062104.00+221554.7 | 58075 | 95.2667 | 22.26522 | *CV? |  |  | 14.57116 | 0.03718
615306105 | J062104.00+221554.7 | 58098 | 95.2667 | 22.26522 | *CV? |  |  |  |
617806105 | J062104.00+221554.7 | 58104 | 95.2667 | 22.26522 | *CV? |  |  |  |
620206105 | J062104.00+221554.7 | 58108 | 95.2667 | 22.26522 | *CV? |  |  |  |
631106105 | J062104.00+221554.7 | 58134 | 95.2667 | 22.26522 | *CV? |  |  |  |
644906105 | J062104.00+221554.7 | 58161 | 95.2667 | 22.26522 | *CV? |  |  |  |
483206105 | J062104.00+221554.7 | 57715 | 95.2667 | 22.26522 |  |  |  |  |
490506105 | J062104.00+221554.7 | 57727 | 95.2667 | 22.26522 |  |  |  |  |
501106105 | J062104.00+221554.7 | 57748 | 95.2667 | 22.26522 |  |  |  |  |
525406105 | J062104.00+221554.7 | 57781 | 95.2667 | 22.26522 |  |  |  |  |
529006105 | J062104.00+221554.7 | 57787 | 95.2667 | 22.26522 |  |  |  |  |
529606105 | J062104.00+221554.7 | 57788 | 95.2667 | 22.26522 |  |  |  |  |
280405205 | J062549.60+145347.4 | 57006 | 96.45668 | 14.89651 | *CV? |  |  | 16.60911 | 0.40176
101201021 | J062811.33+004654.5 | 56295 | 97.04721 | 0.781809 | *CV? |  |  | 14.90546 | 0.08848
419307028 | J062842.43+093209.2 | 57422 | 97.17679 | 9.535889 | *CV? |  |  | 16.76195 | 1.77769
44314014 | J063018.87+053831.3 | 55983 | 97.57867 | 5.642036 | *CV? |  |  | 13.31386 | 0.10978
442712204 | J063018.88+053831.4 | 57468 | 97.57867 | 5.642059 |  |  |  |  |
406804155 | J063209.32+140334.5 | 57400 | 98.03886 | 14.05961 | *CV? |  |  | 18.28807 | 3.54584
44515210 | J063722.97+060755.8 | 55983 | 99.34572 | 6.132189 | *CV? |  |  |  |
111003180 | J065815.75+575142.9 | 56316 | 104.5656 | 57.86194 | *CV? |  |  | 16.01802 | 0.18338
110903180 | J065815.75+575142.9 | 56316 | 104.5656 | 57.86194 |  |  |  |  |
217303180 | J065815.75+575142.9 | 56699 | 104.5656 | 57.86194 |  |  |  |  |
629705190 | J104751.79+483503.7 | 58130 | 161.9658 | 48.58438 | *CV? |  | none | 18.48886 | 0.34744
525913199 | J113835.67+044454.7 | 57782 | 174.6486 | 4.748538 | *CV? |  | none | 19.32805 | 3.88678
587910071 | J204502.35+430520.6 | 58018 | 311.2598 | 43.08906 | *CV? |  | none | 15.39373 | 1.23171
594305076 | J210306.10+402306.3 | 58050 | 315.7754 | 40.38511 | *CV? |  |  | 17.1679 | 0.09375
355016173 | J233900.38+115707.2 | 57283 | 354.7516 | 11.95201 | *CV? |  | none | 18.19033 | 0.27358

Note. —

(This table is available in its entirety in machine-readable form.)

Table 3: Previously known CV candidates from LAMOST DR6 (examples of 15 rows)

obsid | designation | mjd | ra | dec | subtypea | subtypeb | subtypec | medianmagd | magdifferencee
---|---|---|---|---|---|---|---|---|---
| (LAMOST) |  |  |  | (this work) | (Hou2020) | (SIMBAD) | (ZTF) | (ZTF)
266703059 | J001204.51+020129.8 | 56977 | 3.018792 | 2.024947 | CV | CV | none | 16.5676 | 0.42809
592201085 | J001856.93+345444.2 | 58039 | 4.737242 | 34.91228 | CV |  | CV | 17.27818 | 0.7825
592301085 | J001856.93+345444.2 | 58039 | 4.737242 | 34.91228 | CV |  | CV | 17.27818 | 0.7825
614101085 | J001856.93+345444.2 | 58095 | 4.737242 | 34.91228 | CV |  | CV | 17.27818 | 0.7825
75902147 | J002148.44+350451.1 | 56231 | 5.45187 | 35.08089 | CV |  | CV | 14.51631 | 0.88123
284616096 | J002148.44+350451.1 | 57014 | 5.45187 | 35.08089 | CV | CV | CV | 14.51631 | 0.88123
592307039 | J002148.44+350451.2 | 58039 | 5.451867 | 35.08089 | CV |  | CV | 14.51631 | 0.88123
614107039 | J002148.44+350451.2 | 58095 | 5.451867 | 35.08089 | CV |  | CV | 14.51631 | 0.88123
76002147 | J002148.45+350451.1 | 56231 | 5.451892 | 35.08088 | CV |  | CV | 14.51631 | 0.88123
81816096 | J002148.45+350451.2 | 56254 | 5.451877 | 35.08091 | CV |  | CV | 14.43212 | 0.90129
81916096 | J002148.45+350451.2 | 56254 | 5.451877 | 35.08091 | CV |  | CV | 14.43212 | 0.90129
*174106175 | J002500.18+073349.3 | 56601 | 6.250776 | 7.563698 | CV | CV | CV | 19.15958 | 3.25442
483901014 | J002842.53+311819.4 | 57716 | 7.177215 | 31.3054 | CV | CV |  | 18.73505 | 2.02
82609065 | J003005.80+261726.3 | 56255 | 7.524205 | 26.29067 | CV | NL-candidate | NL | 15.2266 | 5.27938
90111206 | J003135.88+434905.3 | 56265 | 7.899519 | 43.81814 | CV |  | Nova | 15.67982 | 0.20626
$\cdots$ | $\cdots$ |  $\cdots$ |  $\cdots$ |  $\cdots$ | $\cdots$ | $\cdots$ | $\cdots$ |  $\cdots$ |  $\cdots$

Note. —

(This table is available in its entirety in machine-readable form.)

Figure 7: Two spectra of J0148 with MJDs 57821 and 57309, respectively, show
variation. The wavelength range is from 3700 Å to 9000 Å. The upper red line
represents the spectrum of MJD 57281, while the lower orange line for MJD
57309. The Balmer lines are indicated with dotted black lines, and the He I,
He II and Fe II lines are indicated with dotted blue lines.  Figure 8: Upper
panel: Two spectra of J0356, taken on MJDs 56617 and 56684, respectively. The
upper red line represents the spectrum of MJD 56617, while the lower orange
line for MJD 56684. The Balmer lines are indicated with dotted black lines,
and the He I, He II and Fe II lines are indicated with dotted blue lines. The
wavelength range is from 3800 Å to 8900 Å. Lower panel: the blue part and the
Ca II triplet region of the spectrum of MJD 56684 show the anti-P Cyg profile.
Figure 9: Three spectra of J0534+3423 are taken at MJDs 56358, 56981 and
57042, respectively. The black, red and orange lines represent the spectrum
taken at MJDs 57042, 56981 and 57042, respectively. The lines of Balmer
series, Na $\lambda$5895.6 and Ca II triplets are indicated with dotted black
lines. The wavelength range is from 3800 Å to 8900 Å.

##  5 Discussion

###  5.1 Objects of New CV Candidates

Among the new CV candidates, we find some of them showing interesting features
and discuss them in the following subsections according to their subtypes.

####  5.1.1 DN in outburst

J0148, J0612, J0701, J0023, and J2112 reveal typically spectral features of
DNs undergoing outbursts, and the wide absorption is visible in each Balmer
line in the spectra of these objects. We note that the Balmer lines for J0148,
J0612, J0701, and J2112 are narrow, for example, the H$\alpha$ is less than 24
Å, while J0023 shows slightly broad emission components (the H$\alpha$
emission core is 33 Å). Asymmetric central absorption also presents at the
Balmer emission cores in all the spectra.

There are two spectra of J0148 (Figure 10 on page 19). The emission of the
Balmer series is significant in the middle of the wide absorption lines, while
He I lines are in emission and does not exhibit wide absorption features.
Thus, we can identify it as a DN in outburst. The other spectrum mainly shows
wide Balmer absorption lines. The variations of the two spectra observed at
different times are shown in Figure 7. The spectrum of MJD 57281 presents
central emission cores of Balmer lines, while H$\alpha$ and He I $\lambda$6678
are almost pure emission. In the spectrum of MJD 57309, the Balmer lines
become nearly all absorption, while the H$\alpha$ still presents an obvious
emission core. Although the multiple emission components with asymmetric
features are not obvious, they can be seen in the absorption lines of Balmer
series higher than H$\alpha$. This means that the emission components have a
steeper decrement than those of the absorption. Besides, He I $\lambda$6678
also shows very weak emission. For these features above, the spectrum of MJD
57309 is likely to be taken during the outburst maximum (Warner, 2003).

The spectrum of J0612 (Figure 10 on page 20) shows narrow asymmetric core
emissions with weak wide absorption in the Balmer series, and the He I
$\lambda$4471, $\lambda$5876, $\lambda$6678, $\lambda$7065 lines are also in
emission.

In the spectrum of J0701 (Figure 10 on page 20), the absorption lines are
strong while the emission cores are weak for the higher order Balmer series.,
and the He I $\lambda$6678 line is affected by the absorption.

The spectrum of J0023 (Figure 10 on page 19) shows strong wide Balmer
absorption with broad emission cores, and there seems to be two emission
components of the Balmer lines. The He II $\lambda$4686 is a strong emission
line, and the C III/N III blend at 4650 Å is also in emission, which may
indicate the presence of a hot emission zone in the disk.

The spectrum of J2112 (Figure 10 on page 20) shows weak wide absorption of
Balmer lines, while the weak He I $\lambda$6678 line presents emission. The C
III/N III blend at 4650 Å is in emission, while He II is absent. Note that Fe
II lines are present in all of the spectra above.

####  5.1.2 Nova-like type

J0409, J0519, J0723, J0454, and J0457 mainly show strong C III/N III emission
features and C II lines, resembling the NL subtype of CVs. The common central
absorption or double-peaked profile in the emission lines suggests the
presence of disks.

J0409 (Figure 10 on page 19) presents peculiar features of a high-temperature
continuum along with obvious H$\alpha$, H$\beta$, He I, He II, C III/N III,
and C II emission lines. The Balmer decrement is quite strong, and the
asymmetric double-peaked profiles present. The He II $\lambda$4686 line and C
III/N III blend are extremely strong, which are much higher than the apparent
strength of H$\alpha$ and H$\beta$. The strength of C III/N III is comparable
to that of He II $\lambda$4686\. The Ca II H/K lines show single narrow
absorption, which could be due to interstellar absorption. Interestingly, we
find that J2023, which is also one of the newly discovered CV candidates, has
very similar features, while the prominent difference is that in J2023 the He
II $\lambda$4686 is absent, for which it can only be classified as a possible
NL.

J0723 (Figure 10 on page 20) shows strong Balmer emission lines but weak He I
lines. The Balmer lines show asymmetric shapes and the strongest Balmer line
is H$\alpha$. For H$\gamma$, an absorption center is present which may
indicate the existence of an accretion disk. The He II $\lambda$4686 is much
weaker than H$\beta$, while the C III/N III blend is as strong as He II
$\lambda$4686\. The Ca II $\lambda$3934 line shows narrow absorption, while C
II $\lambda$7234 is in emission.

J0454 (Figure 10 on page 19) shows very broad strong Balmer, He I, He II, and
C II emission lines. The C III/N III is about half the height of that of He II
$\lambda$4686\. The broad and asymmetric double-peaked shapes in the Balmer
and He lines are significant, which indicates that there is an accretion disk.

The spectra of J0519 (Figure 10 on page 20) and J0457 (Figure 10 on page 19)
have relatively low SNR. He II $\lambda$4686 emission can be identified in
both spectra. The C III/N III emission possibly exists in J0519, while C II
$\lambda$7234 emission is visible in J0457. For J0519, the central absorption
is seen in the Balmer lines, which indicates there is an accretion disk.

####  5.1.3 Magnetic CVs

Four new CV candidates, J0320, J0326, J0216, and J0715, can be classified as
magnetic CVs by the strong He II emission and other spectral features.

J0320 (Figure 10 on page 19) shows strong broad Balmer emission lines, the
strongest being H$\delta$, and they are all blue-shifted. He II $\lambda$4686
is stronger than H$\beta$. He I $\lambda$7281, $\lambda$7065, $\lambda$6678,
$\lambda$5876, $\lambda$4388, $\lambda$4471 and $\lambda$4026 are in emission.
The Na I D line shows very strong absorption, which may be contributed by the
companion star. The C III $\lambda$4634 and C IV $\lambda$5805 lines also show
emission. As He II $\lambda$4686 is strong and Balmer lines split, this object
could be a magnetic CV, possibly of AM Her type.

J0326 (Figure 10 on page 19) reveals an M-type continuum at the red part along
with very strong Balmer and He I/He II emission lines. Both the narrow and
broad emission components in the Balmer and He II lines can be seen, and the
strongest Balmer line is H$\delta$. C II lines only show weak emissions. Deep
Na I D absorption line can be seen at 5895 Å. The strength of He II
$\lambda$4686 is almost the same as H$\beta$, and C III is nearly absent. The
spectroscopic features are consistent with that of the AM Her systems.

J0216 (Figure 10 on page 19) shows a relatively flat continuum with strong
Balmer and He I/He II emission lines. As the He II $\lambda$4686 emission line
is stronger than H$\beta$, and there are mutiple components or splits in the
Balmer lines, we classify it as a magnetic CV, possibly of the AM Her type.

J0715 (Figure 10 on page 20) shows broad Balmer emissions. The H$\alpha$
emission line presents a ‘wine bottle’ profile. The He II $\lambda$4686 line
is quite strong as the strength is comparable to that of H$\beta$, and the C
III/N III blend also show strong emission. In view of the above
characteristics, we consider that it as a possible magnetic CV, specifically
an IP.

####  5.1.4 Binary spectra

The spectra of J0523, J2250, J0535, J0628, J0356 and J2045 present doninent
cool companion star features on the red part. For J2339 and J0311, both the
companion and the white dwarf features can be seen.

The spectrum of J0523 (Figure 10 on page 20) presents strong narrow Balmer
emission lines with weak decrement to higher order. The spectrum shows
prominent M-type star features on the red part, and it can be fitted with a
M2-type star. There is a significant excess at the blue part. The Balmer
series present an asymmetric broadening component superimposing on the strong
narrow emission. He I $\lambda$6678, $\lambda$5876, and $\lambda$4471 lines
are in emission, while He II lines are absent.

J2250 (Figure 10 on page 21) shows a continuum that can be fitted with a late
K-type star, while presents strong Balmer, He I and He II emission lines. The
Balmer lines have a broad component and show obvious asymmetric structure. The
narrow Balmer emission components show a decrement, while the broad components
do not show obvious decrement and shift to blue with respect to the narrow
components. We consider that the narrow Balmer emission may come from
chromospheric activities of the companion star, while the broad one come from
an accretion disk or hot spot. In addition, the deep Na I D $\lambda$5895
absorption line as well as [O I] $\lambda$6302 and O I $\lambda$6365
absorption lines is visible.

J0356 (Figure 11 on page 22) has multiple spectra in LAMOST DR6, and shows
prominent variations. We present two of the spectra of MJDs 56617 and 56684 in
Figure 8. Both spectra show the M-type companion features along with strong
Balmer and He I/He II emission lines. Compared with the fitted template
spectrum, the relative flux of the LAMOST spectra has obvious excesses at the
blue part. In the lower panel of the figure, the near-ultraviolet and near-
infrared regions of the spectrum of MJD 56684 are shown. In the observation of
MJD 56684, the Balmer series members higher than H$\alpha$, He I and Ca II
lines all show anti-P Cyg profile, which reveals the explosive nature of the
object, while He II $\lambda$4686 line appears to be a single emission or have
a P Cyg profile. However, we can not completely rule out the possibility that
it is an explosive pre-main-sequence object (like FU Orionis type stars) by
the spectral features alone.

The spectra of J0535 (Figure 10 on page 20), J0628 (Figure 11 on page 26) and
J2045 (Figure 11 on page 26) all show features of a M-type companion. In those
spectra, the Balmer emission lines are quite narrow and all show asymmetric
features, and the Ca II H/K lines are in strong emission. The H$\beta$ is much
weaker than H$\alpha$, which is not typical for CVs. Moreover, J0535 shows He
I emission lines, while J0628 and J2045 show both He I and He II emission
lines. The Ca II triplets at 8500 Å present slight emission in the former two
but strong in J2045. [O I] $\lambda$6302 also presents weak emission in all
those spectra.

The spectrum of J2339 (Figure 11 on page 27) presents both a M-type companion
and a white dwarf features. In the spectra, both H$\alpha$ and H$\beta$ show
strong emission with double-peaked profiles, and He I $\lambda$6678 presents
weak and broad emission, which may indicates that there is an accretion disk.

J0311 (Figure 11 on page 22) has been classified as a DA-type white dwarf by
Zhang et al. (2013), and they suggested it has unknown emission process. The
spectrum presents strong H$\alpha$ emission core and has strong Balmer
decrement for the emission components, while the broad Balmer absorption
resemble a white dwarf. It also presents strong Ca II triplets but weak He I
emission lines. As there are mutiple emission components in the Balmer series
and some are broad, we consider this object may have a CV nature.

####  5.1.5 Other Objects

There are 21 spectra of J0621 in LAMOST DR6, and 15 of them show H$\alpha$
emission features for which can be identified as a possible CV candidate, as
shown in Figure 11 on pages 24 and 25. The H$\alpha$ emission lines in its
spectra present complex structure. There is a sharp flux increase at the blue
end with emission features. Four spectra of this object taken before MJD 58076
show very wide absorption (the line width of H$\alpha$ beyond 130 Å) of Balmer
and Paschen series, and the Na I absorption lines are strong. Seven spectra
taken at MJD 58076 and later show an unusual double hump at 4000 Å - 5000 Å
for unknown reasons. As these spectra are taken at different dates, the double
hump feature should be real rather than instrumental problems.

J0534 is identified as a possible CV candidate due to the broad H$\alpha$
emission feature. It has three spectra in LAMOST DR6 as shown in Figure 9. The
spectrum of MJD 56358 has a relatively low SNR, in which only symmetrical
H$\alpha$ emission line can be seen. In the spectra of MJDs 56981 and 57042, a
late G- to early K-type companion can be clearly seen. These two spectra
present a strong P Cyg profiles for H$\alpha$ to H$\gamma$ and Ca II triplets,
which could indicate the presence of disk winds or jets. However, in the
spectrum of MJD 56981, both the strength ratio of Sr II $\lambda$4077 to Fe I
$\lambda$4046 and the strength of C/N $\lambda$4215 band indicate that the
K-type star resembles a giant rather than a main-sequence (Gray et al., 2009).
The lack of He emission lines also suggests that it may not be a CV. Follow up
observations are required to determine the nature of this object.

###  5.2 Poorly investigated objects

J0631 was investigated by Balanutsa et al. (2013) using the MASTER-Tunka auto-
detection system. Based on the blue color and photometric variability, it was
identified as a probable dwarf nova. It has also a UV counterpart GALEX J0631.
There are three spectra of this object. Although the SNRs are low for all of
them, the DN properties can be found. In the spectrum of MJD 55861, the
continuum reveals a high-temperature component, and the emission features of
the Balmer and He lines are obvious. The Balmer core emissions are not so
broad and may have mutiple components, while the week wide absorption can be
seen for H$\beta$ and higher Balmer series members. Strong He II $\lambda$4686
emission line also presents in the spectra. The above features are in
consistant with a dwarf nova during an outburst. For the other two spectra of
MJDs 55873 and 57096, the continuum is quite flat. They both present
distinctive asymmetric Balmer and He I $\lambda$6678 emission lines, while He
II $\lambda$4686 is absent in the two spectra. We thus consider both spectra
were taken at the quiescent phase of a DN.

J0720 has been identified to be most likely a polar with an extreme hot spot
(Denisenko, 2018), as it shows a large amplitude of variation presenting in
its light curve with no eclipses found. It has an X-ray counterpart XMMSL2
J0720. There is only one spectrum in LAMOST DR6, which shows sharp and
asymmetric Balmer emission lines with no obvious continuum. He I
$\lambda$4471, $\lambda$6678, and $\lambda$7065 emission lines also present
while He II $\lambda$4686 is absent. These spectral features are consistent
with those of a polar. However, the H$\beta$ shows a double-peaked profile,
which might imply the presence of a residual disk.

##  6 Conclusion

Using the UMAP with k-NN method, we have spectroscopically identified 475 CV
spectra of 323 objects from LAMOST DR6, of which 52 objects are new. After
cross-match with the DR5 CV catalogue presented by Hou et al. (2020), our
results include 215 of their 224 objects in LAMOST DR6 v2. The other nine are
not included as their spectra don’t show obvious H$\alpha$-emission features.
This is due to the limitations of the current UMAP method.

By visually examining the CV features of the spectra, we can remove
contamination of the Oe/Be stars and H II regions, and identify more potential
CV candidates that are previously difficult to single out. The new CVs include
five DNs during outbursts, five NLs and four magnetic CVs (three of AM Her
type and one of IP type). We also discuss objects that directly show the
binary systems. In addition, we listed two objects that have been poorly
investigated from the known CVs in our catalogue. Their spectral features
confirm the subtype they previously classified. Nevertheless, for J0720, the
H$\beta$ presents an double-peaked profile which may suggest that there is a
residual disk.

Our results show that the UMAP algorithm can identify CV spectra that present
an core emission at H$\alpha$, however, there are still shortcomings if the
emission core is weak, the program may not pick them out. More samples are
needed for training of the UMAP method to improve the classification accuracy.
Our results do not include the AM CVn type, and the pre-polars which may only
show broad cyclotron emission lines, because they both lack hydrogen lines.
New methods should be adopted to identify these two types of CVs as they may
not have any H$\alpha$ emission lines.

In the future, we will carry out follow-up photometric studies on the new CV
candidates and poorly studied or interesting CV objects, obtain the orbital
parameters to further study their properties.

## Acknowledgements

We would like to thank the anonymous referee for his/her very heplful
feedback. This work is supported by the National Key Basic R&D Program of
China via 2019YFA0405500, 2019YFA0405501, the National Natural Science
Foundation of China (NSFC) under grant 11773009, 11673007, 12090040, 12090044,
11833006, 11903012, 11703038, 12033003 and 12173103, the Natural Science
Foundation of Hebei Province under grants A2021205006. W.Y.C. is also
supported by the ‘333 talents project’ of Hebei Province. The Guo Shou Jing
Telescope (the Large Sky Area Multi-Object Fiber Spectroscopic Telescope,
LAMOST) is a National Major Scientific Project built by the Chinese Academy of
Sciences. Funding for the project has been provided by the National
Development and Reform Commission. LAMOST is operated and managed by National
Astronomical Observatories, Chinese Academy of Sciences. We acknowledge the
science research grants from the China Manned Space Project with NO.CMS-
CSST-2021-A08. This research has made use of the SIMBAD database, operated at
CDS, Strasbourg, France. This work has made use of data from the Zwicky
Transient Facility (ZTF) data.

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## APPENDIX
THE LAMOST SPECTRA

Figure 10: 21 spectra of 20 new confirmed CV candidates in LAMOST DR6 (ordered
by increasing RA). The wavelength range is from 3700 Å to 9000 Å. Cosmic ray
hits are marked by short red slashes.  Figure 11: 52 spectra of 32 new
possible CV candidates in LAMOST DR6 (ordered by increasing RA). The
wavelength range is from 3700 Å to 9000 Å. Cosmic ray hits are marked by short
red slashes.


labelinglabel 11institutetext: Institute of Applied Mathematical Research
of the Karelian Research Centre of the Russian Academy of Sciences
22institutetext: Petrozavodsk State University 33institutetext: Institute of
Linguistics, Literature and History
of the Karelian Research Centre of the Russian Academy of Sciences,
Petrozavodsk, Russia, 33email<EMAIL_ADDRESS>
http://dictorpus.krc.karelia.ru

# Part of speech and gramset tagging algorithms for unknown words based on
morphological dictionaries of the Veps and Karelian languages††thanks: The
study was supported by the Russian Foundation for Basic Research, grant
18-012-00117.

Andrew Krizhanovsky 1122 0000-0003-3717-2079 Natalia Krizhanovskaya 11
0000-0002-9948-1910 Irina Novak 33 0000-0002-9436-9460

###### Abstract

This research devoted to the low-resource Veps and Karelian languages.
Algorithms for assigning part of speech tags to words and grammatical
properties to words are presented in the article. These algorithms use our
morphological dictionaries, where the lemma, part of speech and a set of
grammatical features (gramset) are known for each word form. The algorithms
are based on the analogy hypothesis that words with the same suffixes are
likely to have the same inflectional models, the same part of speech and
gramset. The accuracy of these algorithms were evaluated and compared. 66
thousand Karelian and 313 thousand Vepsian words were used to verify the
accuracy of these algorithms. The special functions were designed to assess
the quality of results of the developed algorithms. $86.8$% of Karelian words
and $92.4$% of Vepsian words were assigned a correct part of speech by the
developed algorithm. $90.7$% of Karelian words and $95.3$% of Vepsian words
were assigned a correct gramset by our algorithm. Morphological and semantic
tagging of texts, which are closely related and inseparable in our corpus
processes, are described in the paper.

###### Keywords:

Morphological analysis Low-resource language Part of speech tagging.

##  1 Introduction

Our work is devoted to low-resource languages: Veps and Karelian. These
languages belong to the Finno-Ugric languages of the Uralic language family.
Most Uralic languages still lack full-fledged morphological analyzers and
large corpora [5].

In order to avoid this trap the researchers of Karelian Research Centre are
developing the Open corpus of Veps and Karelian languages (VepKar). Our corpus
contains morphological dictionaries of the Veps language and the three
supradialects of the Karelian language: the Karelian Proper, Livvi-Karelian
and Ludic Karelian. The developed software (corpus manager)111See
https://github.com/componavt/dictorpus and the database, including
dictionaries and texts, have open licenses.

Algorithms for assigning part of speech tags to words and grammatical
properties to words, without taking into account a context, using manually
built dictionaries, are presented in the article (see Section 4).

The proposed technology of evaluation (see the section 5) allows to use all
313 thousand Veps and 66 thousand Karelian words to verify the accuracy of the
algorithms (Table 1). Only a third of Karelian words (28%) and two-thirds of
Veps words (65%) in the corpus texts are automatically linked to the
dictionary entries with all word forms (Table 1). These words were used in the
evaluation of the algorithms.

Table 1: Total number of words in the VepKar corpus and dictionary Language |  | The total number
---
of tokens in texts,
$10^{3}$
| N tokens linked to
---
dictionary automatically,
$10^{3}$
| N tokens linked to
---
lemmas having
a complete paradigm,
$10^{3}$
Veps | 488 | 400 (82%) | 313 (65%)
| Karelian
---
Proper
245 | 111 (45%) | 69 (28%)

Let us describe several works devoted to the development of morphological
analyzers for the Veps and Karelian languages.

  * •

The Giellatekno language research group is mainly engaged in low-resource
languages, the project covers about 50 languages [4]. Our project has
something in common with the work of Giellatekno in that (1) we work with low-
resource languages, (2) we develop software and data with open licenses.

A key role in the Giellatekno infrastructure is given to formal approaches
(grammar-based approach) in language technologies. They work with morphology
rich languages. Finite-state transducers (FST) are used to analyse and
generate the word forms [4].

  * •

There is a texts and words processing library for the Uralic languages called
UralicNLP [2]. This Python library provides interface to such Giellatekno
tools as FST for processing morphology and constraint grammar for syntax. The
UralicNLP library lemmatizes words in 30 Finno-Ugric languages and dialects
including the Livvi dialect of the Karelian language (olo – language code).

##  2 Data organization and text tagging in the VepKar corpus

Automatic text tagging is an important area of research in corpus linguistics.
It is required for our corpus to be a useful resource.

The corpus manager handles the dictionary and the corpus of texts (Fig. 1).
The texts are segmented into sentences, then sentences are segmented into
words (tokens). The dictionary includes lemmas with related meanings, word
forms, and sets of grammatical features (in short – gramsets).

Figure 1: Data organization and text tagging in the VepKar corpus.
Total values (e.g. number of words, texts) are calculated for all project
languages.

Text tokens are automatically searched in the dictionary of lemmas and word
forms, this is the first stage (I) of the text tagging, it is not presented at
Fig. 1.

  1. 1.

Semantic tagging. For the word forms found in the dictionary, the lemmas
linked with them are selected (II at Fig. 1), then all the meanings of the
lemmas are collected (III) and semantic relationships are established between
the tokens and the meanings of the lemmas (marked ‘‘not verified’’) (IV).

The task of an expert linguist is to check these links and confirm their
correctness, either choose the correct link from several possible ones, or
manually add a new word form, lemma or meaning.

tokens (words)word formslemmasmeaningsIIIIIIIV not verified

When the editor clicks on the token in the text, then a drop-down list of
lemmas with all the meaning will be shown. The editor selects the correct
lemma and the meaning (Fig. 2).

$\ddagger$See full text online at VepKar:
http://dictorpus.krc.karelia.ru/en/corpus/text/494

Figure 2: Vepsian and Russian parallel Bible translation$\ddagger$ in the
corpus. The editor clicks the word ‘‘Opendai’’ in the text, a menu pops up.
This menu contains a list of meanings collected automatically for this token,
namely: the meaning of the noun ‘‘teacher’’ (‘‘opendai’’ in Veps) and five
meanings of the Veps verb ‘‘opeta’’. The noun ‘‘opendai’’ and the verb
‘‘opeta’’ have the same wordform ‘‘opendai’’. If the editor selects one of the
lemma meanings in the menu (clicks the plus sign), then the token and the
correct meaning of the lemma will be connected (IV stage is verified).

  2. 2.

Morphological tagging. For the word forms found in the dictionary, the
gramsets linked with them are selected (V) and morphological links are
established (VI) between the tokens and the pairs ‘‘word form – gramset’’
(Fig. 1). The expert’s task is to choose the right gramset.

tokens (words)word formsgramsetsIVVI

##  3 Corpus tagging peculiarities

In this section, we describe why the word forms with white spaces and
analytical forms are not taken into account in the search algorithm described
below. Analytical form is the compound form consisting of auxiliary words and
the main word.

The ultimate goal of our work is the morphological markup of the text,
previously tokenized into words by white spaces and non-alphabetic characters
(for example, brackets, punctuation, numbers). Therefore, analytical forms do
not have markup in the texts.

Although we store complete paradigms in the dictionary, including analytical
forms, such forms do not used in the analysis of the text, because each
individual word is analyzed in the text, not a group of words.

For example, we take the Karelian verb ‘‘pageta’’ (leave, run away). In the
dictionary not only the negative form of indicative, presence, first-person
singular ‘‘en pagene’’ is stored, but also connegative (a word form used in
negative clauses) of the indicative, presence ‘‘pagene’’, which is involved in
the construction of five of the six forms of indicative, presence. Thus, in
the text the word ‘en’ (auxiliary verb ‘ei’, indicative, first-person
singular) and ‘pagene’ (verb ‘pageta’, connegative of indicative, presence)
are separately marked.

##  4 Part of speech and gramset search by analogy algorithms

The proposed algorithms operate on data from a morphological dictionary. The
algorithms are based on the analogy hypothesis that words with the _same
suffixes_ are likely to have the same inflectional models and the same sets of
grammatical information (part of speech, number, case, tense, etc.). The
_suffix_ here is a final segment of a string of characters.

Let the hypothesis be true, in that case, if the suffixes of new words
coincide with the suffixes of dictionary words, then part of the speech and
other grammatical features of the new words will coincide with the dictionary
words. It should be noted that the length of the suffixes is unpredictable and
can be various for different pairs of words [1, p. 53].

The POSGuess and GramGuess algorithms described below use the concept of
‘‘suffix’’ (Fig. 3), the GramPseudoGuess algorithm uses the concept ‘‘pseudo-
ending’’ (Fig. 4).

###  4.1 The POSGuess algorithm for part of speech tagging with a suffix

Given the set of words $W$, for each word in this set the part of speech is
known. The algorithm 1 finds a part of speech $\text{pos}_{u}$ for a given
word $u$ using this set.

Data: $P$ – a set of part of speech (POS),
$W=\\{w\;|\;\exists\text{pos}_{w}\in P\\}$ – a set of words, POS is known for
each word, $u\notin W$ – the word with unknown POS, $len(u)$ – the length (in
characters) of the string $u$.

Result: $None$

∃w∈W:w=wprefix+uz// Concatenation of strings

Counter
$\displaystyle\left[\text{pos}^{k}\right]=c^{k},\;k=\overline{1,m},\;\text{where
:}$ $\displaystyle c^{k}\in\mathbb{N},\;c^{1}\geq c^{2}\geq\ldots\geq c^{m},$
$\displaystyle\exists w^{k}_{i}\in
W:w^{k}_{i}={w_{\text{prefix}}}^{k}_{i}\mathbin{+}u_{z}\Rightarrow
c^{k}=|\text{pos}^{k}_{w^{k}_{i}}|,$ $\displaystyle i=\overline{1,c^{k}},$
$\displaystyle\forall i:\text{pos}^{k}_{w^{k}_{i}}=\text{pos}^{k}\in
P,\;\;\;\;a\neq b\Leftrightarrow\text{pos}^{a}\neq\text{pos}^{b}$
$\displaystyle m\;\text{-- the number of different POS of found
words}\;w^{k}_{i}$

1

$z$ = 2 // The position in the string $u$

2 $z_{found}=\text{FALSE}$

3 while $z\leq len(u)$ and $\neg z_{found}$ do

// The suffix of the word $u$ from $z$-th character

4 $u_{z}=\text{ substr }(u,z)\;$ foreach $w\in W$ do

// If the word $w$ has the suffix $u_{z}$ (regular expression)

5 if $w=\sim\textrm{m}/u_{z}\$/$ then

6 $\text{Counter}\left[\text{pos}_{w}\right]++$

$z_{found}=\text{TRUE}$ // Only POS of words with this $u_{z}$ suffix will be
counted. The next "while" loop will break, so the shorter suffix $u_{z+1}$
will be omitted.

7

8 end if

9

10 end foreach

11 z = z + 1

12 end while

// Sort the array in descending order, according to the value

`arsort`( $\text{Counter}\left[\;\right]$ )

Algorithm 1 Part of speech search by a suffix (POSGuess)

In Algorithm 1 we look for in the set $W$ (line 5) the words which have the
same suffix $u_{z}$ as the unknown word $u$. Firstly, we are searching for the
longest substring of $u$, that starts at index z. The first substring
$u_{z=2}$ will start at the second character (line 1 in Algorithm 1), since
$u_{z=1}=u$ is the whole string (Fig. 3).

Then we increment the value $z$ decreasing the length of the substring $u_{z}$
in the loop, while the substring $u_{z}$ has non-zero length, $z\leq len(u)$.
If there are words in $W$ with the same suffix, then we count the number of
similar words for each part of the speech and stop the search.

The Fig. 3 shows the idea of the algorithm 1: for a new word ($kezaman$), we
look for a word form in the dictionary ($raman$) with the same suffix
($aman$).

We begin to search in the dictionary for word forms with the suffix $u_{z=2}$.
If we have not find any words, then we are looking for $u_{z=3}$ and so on.
The longest suffix $u_{z=4}$=‘‘aman’’ with $z=4$ is found.

Then we find all words with the suffix $u_{z=4}$ and count how many of such
words are nouns, verbs, adjectives and so on. The result is written to the
array $Counter[\,]$. In Fig. 3 the _noun_ ‘‘raman’’ was found, therefore we
increment the value of $Counter[noun]$.

Figure 3: Veps nouns in the genitive case ‘‘kezaman’’ (‘‘kezama’’ means
‘‘melted ground’’) and ‘‘raman’’ (‘‘rama’’ means ‘‘frame’’). The word $u$ with
an unknown part of speech is ‘‘kezaman’’. The word $w$ from the dictionary
with the known POS is ‘‘raman’’. They share the common suffix $u_{z}$, which
is ‘‘aman’’.

###  4.2 The GramGuess algorithm for gramset tagging with a suffix

The GramGuess algorithm is exactly the same as the POSGuess algorithm, except
that it is needed to search a subset of gramsets instead of parts of speech.
That is in the set $W$ the gramset is known for each word. The gramset is a
set of morphological tags (number, case, tense, etc.).

###  4.3 The GramPseudoGuess algorithm for gramset tagging with a pseudo-
ending

Let us explain the ‘‘pseudo-ending’’ used in the algorithm GramPseudoGuess.

All word forms of one lemma share a common invariant substring. This substring
is a _pseudo-base_ of the word (Fig. 4). Here the pseudo-base is placed at the
start of a word, it suits for the Veps and Karelian languages. For example, in
Fig. 4 the invariant substring ‘‘huuk’’ is the pseudo-base for all word forms
of the lemma ‘‘huukkua’’. The Karelian verb ‘‘huukkua’’ means ‘‘to call out’’,
‘‘to holler’’, ‘‘to halloo’’.

Figure 4: Wordforms of the Karelian verb ‘‘huukkua’’ (it means ‘‘to call
out’’, ‘‘to holler’’, ‘‘to halloo’’). All word forms have the same pseudo-base
and different pseudo-endings for different set of grammatical attributes
(gramsets).

Given the set of words $W$, for each word in this set a gramset and a pseudo-
ending are known. The algorithm 2 finds a gramset $g_{u}$ for a given word $u$
using this set.

Data: $G$ – a set of gramsets, $W=\\{w\;|\;\exists\,g_{w}\in
G,\exists\,\text{pend}_{w}:w=w_{\text{prefix}}\mathbin{+}\text{pend}_{w}\\}$ –
a set of words, where gramset and pseudo-ending (pend) are known for each
word, $u\notin W$ – the word with unknown gramset, $len(u)$ – the length (in
characters) of the string $u$.

Result: $None$

∃w∈W:pendw=uz Counter
$\displaystyle\left[g^{k}\right]=c^{k},\;k=\overline{1,m},\;\text{where :}$
$\displaystyle c^{k}\in\mathbb{N},\;c^{1}\geq c^{2}\geq\ldots\geq c^{m},$
$\displaystyle\exists w^{k}_{i}\in W:\text{pend}_{w^{k}_{i}}=u_{z}\Rightarrow
c^{k}=|g^{k}_{w^{k}_{i}}|,$ $\displaystyle i=\overline{1,c^{k}},$
$\displaystyle\forall i:g^{k}_{w^{k}_{i}}=g^{k}\in G,\;\;\;\;a\neq
b\Leftrightarrow g^{a}\neq g^{b}$ $\displaystyle m\;\text{-- the number of
different gramsets of found words}\;w^{k}_{i}$

1

$z$ = 2 // The position in the string $u$

2 $z_{found}=\text{FALSE}$

3 while $z\leq len(u)$ and $\neg z_{found}$ do

// The substring of the word $u$ from $z$-th character

4 $u_{z}=\text{ substr }(u,z)\;$ foreach $w\in W$ do

// If the word $w$ has the pseudo-ending $u_{z}$

5 if $\text{pend}_{w}==u_{z}$ then

6 $\text{Counter}\left[g_{w}\right]++$

$z_{found}=\text{TRUE}$ // Only gramsets of words with the pseudo-ending
$u_{z}$ will be counted. The next "while" loop will break, so the shorter
$u_{z+1}$ will be omitted.

7

8 end if

9

10 end foreach

11 z = z + 1

12 end while

// Sort the array in descending order, according to the value

`arsort`( $\text{Counter}\left[\;\right]$ )

Algorithm 2 Gramset search by a pseudo-ending (GramPseudoGuess)

In Algorithm 2 we look for in the set $W$ (line 5) the words which have the
same pseudo-ending $u_{z}$ as the unknown word $u$. Firstly, we are searching
for the longest substring of $u$, that starts at index z.

Then we increment the value $z$ decreasing the length of the substring $u_{z}$
in the loop, while the substring $u_{z}$ has non-zero length, $z\leq len(u)$.
If there are words in $W$ with the same pseudo-ending, then we count the
number of similar words for each gramset and stop the search.

##  5 Experiments

###  5.1 Data preparation

Lemmas and word forms from our morphological dictionary were gathered to _one
set_ as a search space of part of speech tagging algorithm. This set contains
unique pairs ‘‘word – part of speech’’.

In order to search a gramset, we form the set consisting of (1) lemmas without
inflected forms (for example, adverbs, prepositions) and (2) inflected forms
(for example, nouns, verbs). This set contains unique pairs ‘‘word –
gramset’’. For lemmas without inflected forms the gramset is empty.

We put on constraints for the words in both sets: strings must consist of more
than two characters and must not contain whitespace. That is, analytical forms
and compound phrases have been excluded from the sets (see section 3).

###  5.2 Part of speech search by a suffix (POSGuess algorithm)

For the evaluation of the quality of results of the searching algorithm
POSGuess the following function $\text{eval}(\text{pos}^{u})$ was proposed:

$\text{eval}\left(\begin{array}[]{@{}l@{\thinspace}l}\text{pos}^{u},\\\
\text{Counter}&\left[\text{pos}^{k}\right]\rightarrow c^{k},\\\ &\forall
k=\overline{1,m}\\\
\end{array}\right)=\begin{cases}\lx@intercol\text{\small{{\color[rgb]{0,0,1}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,1}The
array Counter[\,] do not contain the correct
$\text{pos}^{u}$.}}}\hfil\lx@intercol\\\
0,&\text{pos}^{u}\neq\text{pos}^{k},\forall k=\overline{1,m},\\\\[8.53581pt]
\lx@intercol\text{\small{{\color[rgb]{0,0,1}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,1}\begin{tabular}[c]{@{}c@{}}First
several POS in the array can have\\\ the same maximum frequency $c^{1}$, one
of this POS is $\text{pos}^{u}$.\end{tabular}}}}\hfil\lx@intercol\\\
1,&\text{pos}^{u}\in\\{\left[\text{pos}^{1},\ldots,\text{pos}^{j}\right]:c^{1}=c^{2}=\ldots=c^{j},j\leq
m\\},\\\\[11.38109pt] \lx@intercol\frac{c^{k}}{\sum_{k=1}^{m}c^{k}},\;\exists
k:\text{pos}^{k}=\text{pos}^{u},\;c^{k}<c^{1}\hfil\lx@intercol\end{cases}$ (1)

This function (1) evaluates the result of the POSGuess algorithm against the
correct part of speech $\text{pos}^{u}$. The POSGuess algorithm counts the
number of words similar to the word $u$ separately for each part of speech and
stores the result in the Counter array.

The Counter array is sorted in descending order, according to the value. The
first element in the array is a part of speech with maximum number of words
similar to the unknown word $u$.

$71\,091$ ‘‘word – part of speech’’ pairs for the Proper Karelian supradialect
and $399\,260$ ‘‘word – part of speech’’ pairs for the Veps language have been
used in the experiments to evaluate algorithms.

During the experiments, two Karelian words were found, for which there were no
suffix matches in the dictionary. They are the word ‘‘cap’’ (English: snap;
Russian: _цап_) and the word ‘‘štob’’ (English: in order to; Russian:
_чтобы_). That is, there were no Karelian words with the endings -p and -b.
This could be explained by the fact that these two words migrated from Russian
to Karelian language.

Figure 5 shows the proportion of Veps and Karelian words with correct and
wrong part of speech assignment by the POSGuess algorithm. Values along the X
axis are the values of the function $\text{eval}(\text{pos}^{u})$, see the
formula (1). This function for evaluating the part of speech assignment takes
the following values:

eval-formula

$4.7$% of Vepsian words and 9% of Karelian words ($x=0$ in Fig. 5) were
assigned the wrong part of speech. That is, there is no correct part of speech
in the result array $Counter[\,]$ in the POSGuess algorithm. This is the first
line in the formula (1).

$2.92$% of Vepsian words and $4.23$% of Karelian words ($x\in[0.1;0.5]$ in
Fig. 5) were assigned the partially correct POS tags. That is, the array
$Counter[\,]$ contains the correct part of speech, but it is not at the
beginning of the array. This is the last line in the formula (1).

$92.38$% of Vepsian words and $86.77$% of Karelian words ($x=1$ in Fig. 5)
were assigned the correct part of speech. The array $Counter[\,]$ contains the
correct part of speech at the beginning of the array.

Figure 5:  The proportion of Vepsian (red curve) and Karelian (blue curve)
words with correct ($x=1$) and wrong ($x=0$) part of speech assignment by the
POSGuess algorithm with the formula (1).

Figure 5 shows the evaluation of the results of the POSGuess algorithm for all
parts of speech together. Table 2 (Veps) and Table 3 (Karelian) show the
evaluation of the same results of the POSGuess algorithm, but they are
presented for each part of speech separately.

Table 2: Number of Vepsian words of different parts of speech used in the experiment. The evaluation of results found by POSGuess algorithm by the formula (1) and fraction of results in percent, where the column 0 means the fraction of words with incorrectly found POS, 1 – the fraction of words with correct POS in the top of the list created by the algorithm. Veps |  | Fraction of not guessed (column 0),
---
partly guessed (0.1–0.5) and guessed (1) POS, %
POS | Words | 0 | 0.1 | 0.2 | 0.3 | 0.4 | 0.5 | 1
Verb | $93\,047$ | 2.12 | 0.52 | 0.55 | 0.47 | 0.36 | 0.01 | 95.97
Noun | $240\,513$ | 2.88 | 0.3 | 0.67 | 0.6 | 0.42 | 0.24 | 94.89
Adjective | $61\,845$ | 12.45 | 1.62 | 1.44 | 1.53 | 1.58 | 0.51 | 80.87
Pronoun | $1244$ | 46.54 | 8.12 | 0.56 | 0.64 | 0 | 0 | 44.13
Numeral | $1200$ | 44 | 6.25 | 2.33 | 0.67 | 0.33 | 0 | 46.42
Adverb | 650 | 64.92 | 3.08 | 2.46 | 1.23 | 0.46 | 0 | 27.85
Table 3: Number of Karelian words of different parts of speech used in the experiment. Karelian |  | Fraction of not guessed (column 0),
---
partly guessed (0.1–0.5) and guessed (1) POS, %
POS | Words | 0 | 0.1 | 0.2 | 0.3 | 0.4 | 0.5 | 1
Verb | $26\,033$ | 3.26 | 0.5 | 0.74 | 0.6 | 0.23 | 0.01 | 94.67
Noun | $36\,908$ | 5.47 | 0.38 | 1.13 | 1.08 | 0.52 | 0.04 | 91.38
Adjective | $6596$ | 35.81 | 6.66 | 4.15 | 4.56 | 2.73 | 0.38 | 45.71
Pronoun | 610 | 81.64 | 2.13 | 0.66 | 3.11 | 2.3 | 0 | 10.16
Numeral | 582 | 65.81 | 1.72 | 1.03 | 0.17 | 1.03 | 0 | 30.24
Adverb | 235 | 68.51 | 3.4 | 2.98 | 2.13 | 0 | 0 | 22.98
Figure 6: Number of Vepsian and Karelian words of different parts of speech
used in the experiment.

###  5.3 Gramset search by a suffix (GramGuess algorithm) and by a pseudo-
ending (GramPseudoGuess algorithm)

$73\,395$ ‘‘word – gramset’’ pairs for the Karelian Proper supradialect and
$452\,790$ ‘‘word – gramset’’ pairs for the Veps language have been used in
the experiments to evaluate GramGuess and GramPseudoGuess algorithms.

A list of gramsets was searched for each word. The list was ordered by the
number of similar words having the same gramset.

For the evaluation of the quality of results of the searching algorithms the
following function $\text{eval}(g^{u})$ has been proposed:

$\text{eval}\left(\begin{array}[]{@{}l@{\thinspace}l}g^{u},\\\
\text{Counter}&\left[g^{k}\right]\rightarrow c^{k},\\\ &\forall
k=\overline{1,m}\\\
\end{array}\right)=\begin{cases}\lx@intercol\text{\small{{\color[rgb]{0,0,1}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,1}The
array Counter do not contain the correct gramset
$g^{u}$.}}}\hfil\lx@intercol\\\ 0,&g^{u}\neq g^{k},\forall
k=\overline{1,m},\\\\[8.53581pt]
\lx@intercol\text{\small{{\color[rgb]{0,0,1}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,1}\begin{tabular}[c]{@{}c@{}}First
several gramsets in the array can have\\\ the same maximum frequency $c^{1}$,
one of these gramsets is $g^{u}$.\end{tabular}}}}\hfil\lx@intercol\\\
1,&g^{u}\in\\{\left[g^{1},\ldots,g^{j}\right]:c^{1}=c^{2}=\ldots=c^{j},j\leq
m\\},\\\\[11.38109pt]
\lx@intercol\frac{c^{k}}{\sum_{k=1}^{m}c^{k}},\;\exists\,k:g^{k}=g^{u},\;c^{k}<c^{1}\hfil\lx@intercol\end{cases}$
(2)

This function (2) evaluates the results of the GramGuess and GramPseudoGuess
algorithms against the correct gramset $g^{u}$.

Table 4: Evaluations of results of gramsets search for Vepsian and Karelian by GramGuess and GramPseudoGuess algorithms.  | GramGuess | GramPseudoGuess
---|---|---
Evaluation | Veps | Karelian | Veps | Karelian
0 | 2.53 | 5.72 | 7.9 | 9.23
0.1 | 0.53 | 0.83 | 1.04 | 1.57
0.2 | 0.71 | 1.16 | 1.24 | 1.37
0.3 | 0.64 | 0.89 | 2.68 | 1.36
0.4 | 0.2 | 0.56 | 0.14 | 0.68
0.5 | 0.11 | 0.09 | 0.83 | 0.43
1 | 95.29 | 90.74 | 86.17 | 85.36

The table 4 shows that the GramGuess algorithm gives the better results than
the GramPseudoGuess algorithm, namely: eval-formula

90.7% of Karelian words were assigned a correct gramset by GramGuess algorithm
versus 85.4% by GramPseudoGuess algorithm;

95.3% of Vepsian words were assigned a correct gramset by GramGuess algorithm
versus 85.4% by GramPseudoGuess algorithm.

It may be suggested by the fact that suffixes are longer than pseudo-endings.
In addition, the GramPseudoGuess algorithm is not suitable for a part of
speech without inflectional forms.

##  6 Morphological analysis results

In order to analyze the algorithm errors, the results of the part-of-speech
algorithm POSGuess were visualized using the Graphviz program. Part-of-speech
error transition graphs were built for Veps language (Fig. 7(a)) and Karelian
Proper supradialect (Fig. 7(b)).

Let us explain how these graphs were built. For example, a thick grey vertical
arrow connects adjective and noun (Fig. 7(b)), and this arrow has labels of
21.6%, 1424 and 3.9%. This means that the POSGuess algorithm has erroneously
identified $1424$ Karelian adjectives as nouns. This accounted for 21.6% of
all Karelian adjectives and 3.9% of nouns. This can be explained by the fact
that the same lemma (in Veps and Karelian) can be both a noun and an
adjective. Nouns and adjectives are inflected in the same form (paradigm).

The experiment showed that there are significantly more such lemmas (noun-
adjective) for the Karelian language than for the Veps language (21.6% versus
9.8% in Fig. 7). Although in absolute numbers Veps exceeds Karelian, namely:
6061 versus 1424 errors of this kind. This is because the Veps dictionary is
larger in the VepKar corpus.

(a) Veps language

(b) Karelian Proper supradialect

Figure 7: Part-of-speech error transition graph, which reflects the results of
the POSGuess algorithm.

##  7 Conclusion

This research devoted to the low-resource Veps and Karelian languages.

Algorithms for assigning part of speech tags to words and grammatical
properties to words are presented in the article. These algorithms use our
morphological dictionaries, where the lemma, part of speech and a set of
grammatical features (gramset) are known for each word form.

The algorithms are based on the analogy hypothesis that words with the same
suffixes are likely to have the same inflectional models, the same part of
speech and gramset.

The accuracy of these algorithms were evaluated and compared. 313 thousand
Vepsian and 66 thousand Karelian words were used to verify the accuracy of
these algorithms. The special functions were designed to assess the quality of
results of the developed algorithms.

71,091 ‘‘word – part of speech’’ pairs for the Karelian Proper supradialect
and 399,260 ‘‘word – part of speech’’ pairs for the Veps language have been
used in the experiments to evaluate algorithms. 86.77% of Karelian words and
92.38% of Vepsian words were assigned a correct part of speech.

73,395 ‘‘word – gramset’’ pairs for the Karelian Proper supradialect and
452,790 ‘‘word – gramset’’ pairs for the Veps language have been used in the
experiments to evaluate algorithms. 90.7% of Karelian words and 95.3% of
Vepsian words were assigned a correct gramset by our algorithm.

If you need only one correct answer, then all three of developed algorithms
are not very useful. But in our case, the task is to get an ordered list of
the parts of speech and gramsets for a word and to offer this list to an
expert. Then the expert selects the correct part of speech and gramset from
the list and assigns to the word. This is a semi-automatic tagging of the
texts. Thus, these algorithms are useful for our corpus.

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