# Morphological Disambiguation of South Sámi with FSTs and Neural Networks

###### Abstract

We present a method for conducting morphological disambiguation for South
Sámi, which is an endangered language. Our method uses an FST-based
morphological analyzer to produce an ambiguous set of morphological readings
for each word in a sentence. These readings are disambiguated with a Bi-RNN
model trained on the related North Sámi UD Treebank and some synthetically
generated South Sámi data. The disambiguation is done on the level of
morphological tags ignoring word forms and lemmas; this makes it possible to
use North Sámi training data for South Sámi without the need for a bilingual
dictionary or aligned word embeddings. Our approach requires only minimal
resources for South Sámi, which makes it usable and applicable in the contexts
of any other endangered language as well.

Keywords: Sámi languages, disambiguation, endangered languages

languageresourceLanguage Resources

Morphological Disambiguation of South Sámi with FSTs and Neural Networks

Mika Hämäläinen, Linda Wiechetek
---
University of Helsinki, UiT The Arctic University of Norway
Finland, Norway
<EMAIL_ADDRESS><EMAIL_ADDRESS>

Abstract content

##  1\. Introduction

Sámi languages are a part of the Uralic language family, and like many other
Uralic languages they are endangered. The languages of this family are
synthetic, meaning that they exhibit a great deal of inflectional and
derivational morphology making their processing with computational means far
from trivial.

In this paper, we present a method for morphological disambiguation of South
Sámi (ISO 639-3 code sma) by using a morphological FST (finite-state
transducer) analyzer and a Bi-RNN (bi-directional recurrent neural network)
trained on North Sámi (ISO 639-3 code sme) data and synthetically generated
South Sámi data. The disambiguation process takes in all the morphological
readings produced by the FST and uses the neural network to pick the
contextually correct disambiguated reading.

North and South are not direct neighbors in the dialect continuum, but share a
big part of the lexicon and many grammatical features like an elaborate case
system, non-finite clause constructions, a large amount of verbal and nominal
derivations. However, they have a number of distinctions in lexicon,
morphology and syntax.

One of the important differences is the omission of the copula verb in South
Sámi, but not or less so in North Sámi. The typical word order is SOV
(subject-object-verb) in South Sámi, and SVO (subject-verb-object) in North
Sámi. The case system is slightly different as well. South Sámi distinguishes
between inessive (place) and elative (source) case [Bergsland, 1994]. In North
Sámi, this is synthesized in one morpho-syntactic case, called locative case.

In addition to the aforementioned differences, also the homonymies are not the
same. In North Sámi, regular noun homonymies are genitive/accusative and
comitative singular/locative plural. In South Sámi, on the other hand, they
are illative plural/accusative plural and essive (underspecified as regards
number)/inessive plural/comitative singular.

Sentence | Gos dáppe lea máddi? ‘Where is the South here?’
---|---
FST output |  | [’gos+Adv+Subqst’, ’gos+Adv’], [’dáppe+Adv’], [’leat+V+IV+Ind+Prs+Sg3’],
---
[’máddat+V+TV+Imprt+Du2’, ’máddat+V+TV+PrsPrc’, ’máddi+N+Sg+Nom’], [?+CLB]
Source sequence | Adv Subqst _ Adv _ IV Ind Prs Sg3 V _ Du2 Imprt N Nom PrsPc Sg TV V _ CLB
Target sequence |  | Adv _ Adv _ Mood=Ind Number=Sing Person=3 Tense=Pres VerbForm=Fin V _
---
Case=Nom Number=Sing N _ CLB
Table 1: An example of the training data

Even in the context of morphologically rich languages, a simple POS (part-of-
speech) tagging is often not enough as it only reduces some of the ambiguity,
and is not enough for lemmatization, for instance. Then again, without
lemmatization and the small amount of data available for these languages,
modern NLP methods such as word embeddings cannot be as reliably used as in
the case of majority languages.

South Sámi, with its estimated number of 500 speakers, is categorized as
severely endangered by UNESCO [Moseley, 2010] and is spoken in Norway and
Sweden. The language is spoken in Norway and Sweden and its bilingual users
frequently face bigger challenges regarding literacy in the lesser used
language than in the majority language due to reduced access to language
arenas [Outakoski, 2013, Lindgren et al., 2016].

The central tools used for disambiguation of Sámi languages are _finite state
transducers_ and _Constraint Grammars_. Constraint Grammar is a rule-based
formalism for writing disambiguation and syntactic annotation grammars
[Karlsson, 1990, Karlsson et al., 1995]. Constraint Grammar relies on a
bottom-up analysis of running text. Possible but unlikely analyses are
discarded step by step with the help of morpho-syntactic context. The vislcg3
implementation111http://visl.sdu.dk/constraint_grammar.html (accessed
2018-10-08), also [Didriksen, 2010] is used in particular.

South Sámi has several Constraint Grammars including a morpho-syntactic
disambiguator, a shallow syntactic analyzer, and a dependency analyzer
[Antonsen et al., 2010, Antonsen and Trosterud, 2011]. Antonsen and Trosterud
(2011) use a fairly small Constraint Grammar (115 rules) for South Sámi part
of speech (POS) and lemma disambiguation, resulting in a precision of 0.87 and
a recall of 0.98 for full morpho-syntactic disambiguation. While these are
very good results with a comparatively small workload, they require the work
of a linguist with knowledge of the language or a linguist and a language
expert in addition. However, we want to show how grammatical tools can be
built in the absence of these.

Whereas our paper deals with South Sámi disambiguation, the main purpose of
this work is to demonstrate that a disambiguator can be built with relatively
few resources based on a morpho-syntactically related language. This is
useful, not only in the wider context of Sámi languages, but also for other
endangered languages as it provides the language community quickly with much-
needed resources while there are children - the future speakers - learning the
language. Our approach follows the previously established ideology for using
FSTs together with neural networks to solve the problem of disambiguation [Ens
et al., 2019].

##  2\. Related Work

Parallel texts have been used to deal with morphological tagging in the
context of low-resourced languages [Buys and Botha, 2016]. They use aligned
parallel sentences to train their their Wsabie-based model to tag the low-
resource language based on the morphological tags of a more resourced language
sentences in the training data. A limitation of this approach is that the
morphological relatedness of the high-resource and low-resource languages has
to be high.

Andrews et al. [Andrews et al., 2017] have proposed a method for POS (part of
speech) tagging of low-resource languages. They use a bilingual dictionary
between a low-resource and high-resource language. In addition, their system
requires monolingual data for building cross-lingual word embeddings. The
resulting POS tagger is trained on an LSTM neural network, and their approach
performs consistently better than the other approaches on the benchmarks they
report.

Lim et al. [Lim et al., 2018] present an approach for syntactic parsing of
Komi-Zyrian and North Sámi data using multilingual word embeddings. They use
pre-trained word-embeddings of two high-resource languages; Finnish and
Russian. Then they train monolingual word-embeddings for the low-resource
languages from small corpora. They project these individual word embeddings
into a single space by using bilingual dictionaries for alignment. The parser
was implemented as an LSTM based model, and its performance is higher for POS
tagging than for syntactic parsing. The most important finding for our
purposes is that including a related high-resource language improves the
accuracy of their method.

DsDs [Plank and Agić, 2018] is a neural network based part-of-speech tagger
intended to be used in the context of low-resource languages. Their core idea
is to use a bi-LSTM model to project POS tags from one language to another
with the help of lexical information and word embeddings. Their experiments in
a low-resource setting reveal that including word embeddings can boost the
model, but lexical information can also help to a smaller degree.

The scope of a great part of the related work is limited to POS tagging.
Nevertheless, the morphologically rich Uralic languages call for a more full
blown morphological disambiguation than a mere POS tagging in order to make
higher-level NLP tools usable for these languages. Moreover, our approach
cannot count on the existence of high-quality bilingual dictionaries between
morphologically similar languages nor aligned word embeddings, as such
resources are not easily available for endangered languages.

##  3\. Data and Tools

The training data for South Sámi disambiguation comes from the Universal
Dependencies Treebank of the related North Sámi language sheyanova:2017. Out
of all the Sámi languages, North Sámi has by and large the biggest amount of
NLP resources available and therefore its use as a starting point for related
languages makes perfect sense. The treebank consists of 26K tokens and comes
pre-divided into a training and testing datasets.

In addition to the treebank, we use FSTs for both North Sámi and South Sámi
with UralicNLP uralicnlp_2019. These transducers are integrated in the open
GiellaLT infrastructure Moshagen2014 for Uralic languages. The FSTs take in a
word in an inflectional form and produce all the possible morphological
readings for it.

In order to evaluate our system, we use a small dataset for South Sámi that
has been disambiguated automatically by a Constraint Grammar and checked
manually. Currently, the dataset is not publicly available. The data consists
of 1994 disambiguated sentences and we only use it for the evaluation.

| North Sámi | South Sámi
---|---|---
Average | 3.1 | 1.8
Table 2: Average ambiguity

Table 2 shows the average morphological ambiguity in the North Sámi training
set and South Sámi test set when the FSTs are used to produce all
morphological readings for every word in the corpus. As we can see, North Sámi
exhibits a much higher degree of morphological ambiguity than South Sámi.

For generating more data, we use the South Sámi lemmas from the South Sámi-
Norwegian dictionary located in the GiellaLT infrastructure Moshagen2014. The
dictionary has 11,438 POS tagged South Sámi lemmas. We only use this
dictionary for South Sámi words and omit all the Norwegian translations in our
method.

Template | Target morphology
---|---
(N Sg Nom) (N Sg Ill) (V IV Ind Prs Sg3) |  | (N Case=Nom Number=Sing) (N Case=Ill Number=Sing)
---
(V Mood=Ind Number=Sing Person=3 Tense=Pres VerbForm=Fin)
(N Sg Nom) (Adv) (V TV Ger) | (N Case=Nom Number=Sing) (Adv) (V VerbForm=Ger)
(N Sg Nom) (N Sg Ine) | (N Case=Nom Number=Sing) (N Case=Ine Number=Sing)
mannem (N Sg Acc) (V TV Ind Prs Sg1) |  | (Pron Case=Acc Number=Sing Person=1 PronType=Prs)
---
(N Case=Acc Number=Sing)
(V Mood=Ind Number=Sing Person=1 Tense=Pres VerbForm=Fin)
(N Sg Nom) (N Sg Ela) (V IV Ind Prs Sg3) |  | (N Case=Nom Number=Sing) (N Case=Ela Number=Sing)
---
(V Mood=Ind Number=Sing Person=3 Tense=Pres VerbForm=Fin)
altemse (V TV Ind Prs Sg1) (N Ess) |  | (Pron Case=Acc Number=Sing Person=3 PronType=Prs)
---
(V Mood=Ind Number=Sing Person=1 Tense=Pres VerbForm=Fin)
(N Case=Ess)
Table 3: Templates for generating South Sámi data

##  4\. Neural Disambiguation

We train a sequence-to-sequence Bi-RNN model using OpenNMT [Klein et al.,
2017] with the default settings except for the encoder where we use a BRNN
(bi-directional recurrent neural network) instead of the default RNN
(recurrent neural network) as BRNN has been shown to provide a performance
gain in a variety of tasks. We use the default of two layers for both the
encoder and the decoder and the default attention model, which is the general
global attention presented by Luong et al. [Luong et al., 2015].

We experiment with two models, one that is trained with the North Sámi
Treebank only, and another one that is trained with South Sámi text generated
by templates and the North Sámi data. Both models are trained for 60,000
training steps with the same random seed value.

The North Sámi data gives us the target sequence, that is the correct
morphological tags and the POS tag. However, the source sequence has to be
generated automatically before the training. For this, we use the North Sámi
FST analyzer. We produce all the possible morphologies for each word in the
Treebank. The training is done from a sorted list of homonymous readings for
each word separated by a character indicating word boundary to the
disambiguated set of homonymous readings from the UD (universal dependencies)
TreeBank on a sentence level. In other words, the only thing the model sees
are morphological tags on the source and the target side. Lemmas and words are
dropped out so that the model can be used for South Sámi without the need of
aligned word embeddings or dictionaries. This is illustrated in Table 1.

For producing synthetic data, we wrote six small templates that reflect some
common morpho-syntactic differences between South Sámi and North Sámi. This
is, for instance, the absence of elative and inessive case in North Sámi, both
of which are merged into the single locative case. For each template, we
produce 20 different ambiguous sentences by selecting words fitting to the
template at random from the South Sámi dictionary and inflecting them
accordingly with the FST. Once the words are inflected, we can analyze them to
get the ambiguous reading. The templates can be seen in Table 3.

##  5\. Results and Evaluation

We evaluate the models, the one trained only with the North Sámi Treebank and
the one that had additional template generated training data, with the
disambiguated gold standard that exists for South Sámi. As the South Sámi gold
standard follows the GiellaLT FST tags, we converted the tags automatically
into UD format, since the neural network is trained to output UD tags.

The evaluation results are shown in Table 4. The first column shows the
percentage of sentences that have been fully disambiguated correctly, the
second columns shows this on a word level i.e. how many words were fully
correctly disambiguated and finally the last column shows the accuracy in POS
tagging. The results indicate that adding the small synthetically generated
data to the training boosted the results significantly.

|  | Fully correct
---
sentences
| Fully correct
---
words
POS correct
| N. Sámi
---
only
12.0% | 37.6% | 59.7%
| N. Sámi &
---
templates
13.0% | 42.2% | 66.4%
Table 4: Evaluation results of the two different models on South Sámi data

As for the incorrectly disambiguated morphological readings, there is a degree
to how incorrect they are. This is shown in Table 5, which shows the errors
based on how many morphological tags were predicted wrong. In both cases, more
than half of the wrongly disambiguated words only differ by one tag from the
gold standard. The results for the model trained on the additional template
data show that the errors the model makes are still closer to the correct
reading.

| 1 tag | 2 tags | 3 tags | more tags
---|---|---|---|---
| North Sámi
---
only
58.4% | 11.8% | 15.8% | 14.0%
| North Sámi
---
& templates
60.8% | 12.1% | 15.1% | 12.0%
Table 5: Errors based on the number of erroneous morphological tags on a word
level

Below, we are having a closer look at the actual sentences and their analyses,
shedding some light on the shortcomings the neural network and suggesting
improvements. In ex. 5, our system erroneously picks the nominal singular
nominative instead of the adverb reading for daelie. The nominal reading,
however, is very rare.

. Daelie dle geajnam gaavnem!
now;thensg.nom so street.acc find.prs.sg1
‘Then I find the street!’

Negation verbs pose a problem to the neural network. Of the 238 instances only
very few negation verbs - despite not being homonymous with any other forms -
are analyzed as such. In ex. 5, im ‘I don’t’ is analyzed a an indicative past
tense verb 1st person singular (the last of which is correct) despite the fact
that im is not ambiguous.

. Im sïjhth gåabph gih, men tjidtjie jeahta månnoeh.
not.neg.sg1 want.conneg anywhere then, but mother says us.du1.nom
‘I don’t want anywhere then, but mother says us two will go.’

There are other difficulties related to negation in the system. In the
following example, the neural network predicts more tokens than the sentence
contains, i.e. a negative verb (correctly) and a connegative form
(erroneously) usually preceded by the negative verb.

. - Aellieh!
\- not.neg.imprt.sg2
‘- Don’t!’

##  6\. Discussion and Conclusions

Uralic languages are highly ambiguous in terms of their morphology, and the
linguistic resources such as annotated corpora for these languages are quite
limited. This poses challenges in the use of modern NLP methods that have been
successfully employed on high-resource languages. In order to overcome these
limitations, we proposed a representation based on the ambiguous morphological
tags of each word in a sentence.

We have presented a viable way of disambiguation for South Sámi based on an
FST and training data on North Sámi with minimal templates needed to cover
some of the morpho-syntactic differences of the two languages. The preliminary
results look promising, especially since there are nine different Sámi
languages. Not to mention similar situations for other endangered languages,
where data for a similar language is available.

Our method is more of a hybrid pipeline of rule-based FSTs that produce the
possible morphological readings and a neural network that does the
disambiguation. This makes it possible to replace the FST with some other
rule-based solution or a neural network based morphological analyzer, given
that recent research has shown promising results for the use of neural
networks in morphology of endangered languages [Schwartz et al., 2019,
Silfverberg and Tyers, 2019].

Moreover, our pipeline can be further enhanced by rules. In our experiments,
we had the neural network disambiguate out of all the possible morphological
readings. Instead of doing that, it is possible to disambiguate first with a
rule-based tool such as a Constraint Grammar, and use the neural network to
disambiguate the remaining ambiguity. That way we do not need to guess what we
already know. It is particularly important to make sure that if the morphology
is known, the neural network would not be used to guess it again. This would
allow for combining the best of the two worlds; the accuracy of the rule-based
methods and the scalability of a neural network.

An interesting question for the future is how far one could get in
disambiguation with our proposed method if one was only to train the model by
using templates. As even a small number of templates was enough to improve the
results noticeably, an entirely template based approach does not seem to be
entirely out of the question. Especially if the templates were constructed
with more generative freedom such as by following a formalism deriving from
CFG (context-free grammar). The use of synthetically generated source data is
known to improve NMT (neural machine translation) models when the target data
is of a high quality (see [Sennrich et al., 2016]). Also, some promising work
has been conducted in fully synthetically generated parallel data in NMT
[Hämäläinen and Alnajjar, 2019].

This year has been particularly good for Uralic languages with small UD
Treebanks recently published for Skolt Sámi, Karelian, Livvi, Komi-Permyak and
Moksha. This means that in the future we can try different variations of our
method with these languages as well with minimal modifications to the current
approach as all of these languages have rule-based FSTs available in the
GiellaLT infrastructure.

##  7\. Acknowledgments

We would like to thank Lene Antonsen and Anja Regina Fjellheim Labj for their
work on the South Sámi Constraint Grammar disambiguator within the GiellaLT
infrastructure and for making their automatically annotated and manually
corrected South Sámi corpus available to us.

##  8\. Bibliographical References

## References

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  * Antonsen and Trosterud, 2011 Antonsen, L. and Trosterud, T.  (2011).  Next to nothing – a cheap south saami disambiguator.  pages 131–137, 05.
  * Antonsen et al., 2010 Antonsen, L., Wiechetek, L., and Trosterud, T.  (2010).  Reusing grammatical resources for new languages.  In Proceedings of the 7th International Conference on Language Resources and Evaluation (LREC 2010), pages 2782–2789, Stroudsburg. The Association for Computational Linguistics.
  * Bergsland, 1994 Bergsland.  (1994).  Sydsamisk grammatikk.  Davvi Girji.
  * Buys and Botha, 2016 Buys, J. and Botha, J. A.  (2016).  Cross-lingual morphological tagging for low-resource languages.  In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1954–1964. Association for Computational Linguistics.
  * Didriksen, 2010 Didriksen, T., (2010).  Constraint Grammar Manual: 3rd version of the CG formalism variant.  GrammarSoft ApS, Denmark.
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  * Hämäläinen and Alnajjar, 2019 Hämäläinen, M. and Alnajjar, K.  (2019).  A template based approach for training nmt for low-resource uralic languages-a pilot with finnish.  In Proceedings of the 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence, pages 520–525.
  * Karlsson et al., 1995 Karlsson, F., Voutilainen, A., Heikkilä, J., and Anttila, A.  (1995).  Constraint Grammar: A Language-Independent System for Parsing Unrestricted Text.  Mouton de Gruyter, Berlin.
  * Karlsson, 1990 Karlsson, F.  (1990).  Constraint Grammar as a Framework for Parsing Running Text.  In Hans Karlgren, editor, Proceedings of the 13th Conference on Computational Linguistics (COLING 1990), volume 3, pages 168–173, Helsinki, Finland. Association for Computational Linguistics.
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  * Lim et al., 2018 Lim, K., Partanen, N., and Poibeau, T.  (2018).  Multilingual dependency parsing for low-resource languages: Case studies on north saami and komi-zyrian.  In Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018).
  * Lindgren et al., 2016 Lindgren, E., Sullivan, K., Outakoski, H., and Westum, A.  (2016).  Researching literacy development in the globalised North: studying tri-lingual children’s english writing in Finnish, Norwegian and Swedish Sápmi.  In David R. Cole et al., editors, Super Dimensions in Globalisation and Education, Cultural Studies and Transdiciplinarity in Education, pages 55–68. Springer, Singapore.
  * Luong et al., 2015 Luong, M.-T., Pham, H., and Manning, C. D.  (2015).  Effective approaches to attention-based neural machine translation.  arXiv preprint arXiv:1508.04025.
  * Moseley, 2010 Christopher Moseley, editor.  (2010).  Atlas of the World′s Languages in Danger.  UNESCO Publishing, 3rd edition.  Online version: http://www.unesco.org/languages-atlas/.
  * Outakoski, 2013 Outakoski, H.  (2013).  Davvisámegielat čálamáhtu konteaksta [The context of North Sámi literacy].  Sámi diedalaš áigečála, 1/2015:29–59.
  * Plank and Agić, 2018 Plank, B. and Agić, Ž.  (2018).  Distant supervision from disparate sources for low-resource part-of-speech tagging.  In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 614–620. Association for Computational Linguistics.
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  * Silfverberg and Tyers, 2019 Silfverberg, M. and Tyers, F.  (2019).  Data-driven morphological analysis for uralic languages.  In Proceedings of the Fifth International Workshop on Computational Linguistics for Uralic Languages, pages 1–14, Tartu, Estonia, January. Association for Computational Linguistics.

##  9\. Language Resource References

lrec languageresource


#  Spectroscopic studies of an ultraluminous supersoft X-ray source in M81

Yu Bai, JiFeng<EMAIL_ADDRESS>and Song Wang  Key
Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese
Academy of Sciences, 20A Datun Road, Chaoyang Distict, 100012 Beijing, China

###### Abstract

Ultraluminous supersoft X-ray sources (ULS) exhibit supersoft X-ray spectra
with blackbody temperatures below 0.1 keV and bolometric luminosities above
1039 ergs s-1. In this Letter we report the first optical spectroscopic
observations of a ULS in M81 using the LRIS spectrograph on the Keck I
telescope. The detected Balmer emission lines show the mean intrinsic velocity
dispersion of 400 $\pm$ 80 km s-1, which are consistent with from an accretion
disk. The spectral index of the continuum on the blue side is also consistent
with the multi-color disk model. The Hα emission line exhibits a velocity of
$\sim$ 180 km/s relative to the local stellar environment, suggesting this ULS
is possibly a halo system in M81 belonging to an old population. No
significant shift is found for the Hα emission line between two observations
separated by four nights.

###### Subject headings:

galaxies: individual (M81) — X-rays: binaries

##  1\. Introduction

Ultraluminous supersoft X-ray sources (ULSs) are pointlike, non-nuclear X-ray
sources having extremely soft spectra with equivalent blackbody temperatures
below 0.1 keV and bolometric luminosities above 1039 ergs s-1. They are
thought to be massive white dwarfs (WDs) burning accreted material on their
surface or intermediate-mass black holes (IMBHs; 10${}^{2}-$104 $M_{\odot}$)
with sub-Eddington accretion (Liu & Di Stefano, 2008).

Swartz et al. (2002) observed the nearby spiral galaxy M81 with Chandra ACIS,
and discovered an intriguing ULS in the bulge, R.A. = 09h55m42.2s, dec. =
$+$69°03′36.5″ (J2000.0) (heareafter M81 ULS1). Its spectrum can be fitted by
a blackbody model with a temperature of $\sim$ 70 eV. The bolometric
luminosity calculated with the distance of M81 is $\sim$ 1039 ergs s-1. The
follow-up X-ray study reveals that its spectrum can be described by either a
blackbody for a WD or a multi-color accretion disk for an IMBH (Liu, 2008).

Its optical counterpart was detected by Hubble Space Telescope ($HST$; Liu &
Di Stefano 2008). The spectral energy distribution (SED) from broad-bands
photometry exhibits a blue and a red component. The spectral index of the blue
component is consistent with a geometrically thin accretion disk, and the red
component could be described by an AGB star. The SED also shows excessive Hα
emission which is probably originated from the accretion disk or surrounding
material photo-ionized by the soft X-ray emission of M81 ULS1, but the band
photometry results cannot provide the width of the emission line known as an
indicator of physical process.

The SED measurements, however, suffer from the intrinsic variabilities of
ULS1, since a flux decrease by a factor of $\sim$ 2.4, as Tao et al. (2011)
presented, occurred in less than a week in optical wavelength. Spectral
observations, on the other hand, are independent of the variabilities. The
expected Balmer emission lines in the spectrum will enable us not only to
characterize its physical conditions but also to probe its local environments
(Moon et al., 2011). This information is essential for us to understand the
nature of M81 ULS1.

In this Letter we report Keck spectroscopic observations of M81 ULS1 and
present evidence for the existence of an accretion disk. Section 2 describes
the Keck/LRIS observations and the result. The discussion is given in Section
3.

##  2\. Data Analysis and Results

The observations of M81 ULS1 occurred on 2010 April 13 and 2010 April 17
during its expected X-ray-low state (Wang et al., 2015) using the Low
Resolution Imaging Spectrograph (LRIS) on the Keck I 10m telescope. Three
exposures of 1000 s were taken in the first night and two of 1200 s in the
second night. The mean seeings in $B$ band were 0.6$\arcsec$ and 0.8$\arcsec$
respectively. The light of the counterpart was masked with a 0.7″ wide slit
and split with a beam dichroic to the blue and red sides followed by a 300
lines mm-1 and a 400 lines mm-1 grating.

The spectra were reduced in a standard way with IRAF. First of all, raw FITS
files were bias-subtracted, flat-corrected, and combined. On the blue side of
the spectra, the position of the optical counterpart along the slit was
verified by comparing its position in the target acquisition image (Figure 1b)
with an HST ACS F606W image (Liu & Di Stefano, 2008). On the red side, since
the counterpart was not obvious along the slit, an offset between the
counterpart and the WD (PG$1708+602$) was used to verify the position of the
counterpart.

Subsequently, raw spectra of the counterpart were extracted with an aperture
size of 1″. The wavelength calibration was then carried out based on the line
lists given in the manual of Keck111http://www2.keck.hawaii.edu/inst/lris/..
The precision of the calibration is 0.2 Å which is obtained from RMS (root
mean square) of arc-lamp fitting.

Finally, PG$1708+602$ was used as the standard star to calibrate the flux by
applying the standard flux given by Massey & Strobel (1990). Since the
standard flux of PG$1708+602$ covered the wavelength from 3126 Å to 8004 Å, we
had to extend the tabulated values to 10000 Å for the calibration on the red
side (the upper panel in Figure 2).

Figure 1.—  a: False-color Spitzer map of M81 used 5.8 $\mu$m ($blue$), 8.0
$\mu$m ($green$) and 24 $\mu$m ($red$). The blue line is 2″ slit in Vega
Beltrán et al. (2001). The light green rectangle presents the sky covered by
Keck. b: The target acquisition image (gray) on the blue side and the image of
0.7″ wide slit (red) in April 13. The green circle gives the position of the
optical counterpart in a radius of 1″. c: A section of the 2-D dispersed image
in April 13. The yellow arrows mark the locations of Hβ, Hα and the unknown
emission line around 5530 Å. The red rectangle marks the Hα absorption line in
a region corresponding to the red rectangle in b.  Figure 2.— Upper panel:
Observational result of PG$1708+602$. The fluxes are in the unit of 10-14 erg
s-1 cm-2 Å-1. Middle panel: The spectrum of optical counterpart. The
integrated spectra of counterpart are smoothed to illustrate features clearly.
Grey lines plot error spectra in 1 $\sigma$ and the positions of hydrogen
lines are given in red. The fluxes are in the unit of 10-18 erg s-1 cm-2 Å-1
respectively. Lower panel: Emission lines of Hβ, Hα and the emission line
around 5530 Å after the subtraction of the continuum. Red lines stand for
emission lines obtained in 2010 April 13, blue for lines obtained in 2010
April 17 and black for combined emission lines. The fluxes are in the unit of
10-18 erg s-1 cm-2 Å-1.  Figure 3.—  SED of M81 ULS1. The blue and red
components suggested by Liu & Di Stefano (2008) are plotted in red lines. Red
points are photometric results of GALEX and squares are those of HST obtained
from Liu & Di Stefano (2008). For correction of Galactic extinction, we have
adopted the Galactic color excesses, $E(B-V)$=0.08, given by Schlegel et al.
(1998), and the parameterization of the Galactic extinction law given by
Cardelli et al. (1989) with an extinction ratio $R_{V}=3.1$, and the
conversion factors for the FUV and NUV are $A_{FUV}=7.9E(B-V)$ and
$A_{NUV}=8.0E(B-V)$.  Table 1Line fitting results

| Center (Å) | FWHM (Å)
---|---|---
| 4.13 | 4.17 | Combined | 4.13 | 4.17 | Combined
Hβ | 4861.4$\pm$1.1 | 4861.2$\pm$0.3 | 4861.3$\pm$0.6 | 8.8$\pm$2.7 | 8.2$\pm$0.7 | 8.6$\pm$1.3
$\sim$5530Å | 5532.3$\pm$1.5 | 5543.1$\pm$1.6 | 5536.6$\pm$1.6 | 32.5$\pm$3.5 | 32.9$\pm$4.0 | 32.2$\pm$3.8
Hα | 6562.9$\pm$0.2 | 6562.8$\pm$0.2 | 6562.8$\pm$0.2 | 10.8$\pm$0.5 | 9.4$\pm$0.3 | 10.2$\pm$0.4
Hα,ab∗ | 6558.8$\pm$0.5 | 6558.9$\pm$0.3 |  |  |  |

Note. — ∗Calculated for the Hα absorption as marked by the red rectangle in
Fig1c.

Table 2GALEX Observations and results for M81 ULS1

Tile | Band | Effective | Exptime | Min | Max | Inner | Outer | AB Mag.
---|---|---|---|---|---|---|---|---
|  | Wavelength |  | ObsDate | ObsDate | Radius | Radius |
(1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9)
GI1_071001_M81 | FUV | 1538.6 | 14706.7 | 2006-01-05 | 2007-03-31 | 6.3 | 10.5 | 22.27$\pm$0.10
| NUV | 2315.7 | 29421.5 | 2005-01-12 | 2007-03-31 | 7.9 | 13.2 | 21.36$\pm$0.17

Note. — Col. (1): Tile name. Col. (2): Band name. Col. (3): Effective
wavelength in angstrom. Col. (4): Total exposure time in seconds. Col. (5):
Earliest observation UT date for visits which make up the coadd. Col. (6):
Latest observation UT date. Col. (7): Inner radius in arcsecond for signal
integration Morrissey et al. (2007). Col. (8): Outer radius in arcsecond for
background substraction. Col. (9): Magnitudes in AB system without correction
of Galactic extinction.

###  2.1. Balmer Emission Lines

As shown in Figure 2, the Balmer emission lines are notable features in the
spectra. Here we used $\chi^{2}$ minimization to fit the centers and FWHMs
(full width at half-maximum) of Hβ, Hα and another notable emission line
around 5530Å. The results with 1$\sigma$ errors are listed in Table 1. The
mean FWHM of Hβ and Hα emission lines derived from the fitting is 490 $\pm$ 80
km s-1, which is significantly larger than the spectral resolution, 280 $\pm$
10 km s-1measured from Hg I $\lambda$5461 in the arc lamp spectrum. The
intrinsic dispersion is 400 $\pm$ 80 km s-1for the Balmer emission lines. The
radial velocity of ULS1 is consistent with zero (an average of 2 $\pm$ 23 km
s-1) within the precision of wavelength calibration, which implies that M81
ULS1 is unlikely a distant AGN with a large receding velocity.

The Balmer emission lines are mainly seen in spectra of H II regions,
planetary nebulae and accretion disks around compact objects. The line
dispersions for H II regions and planetary nebulae should be comparable to
instrumental dispersions (Fang et al., 2013; Nicholls et al., 2014), while for
accretion disks the line dispersions range from a few hundreds to thousands of
kilo meters per second. The detected Balmer emission lines of M81 ULS1 are
significantly broader than the instrumental dispersion, and are consistent
with from an accretion disk around a compact object.

The observations in two nights enable us to measure the shift of Hα related to
the binary motion. We used the technology of the cross-correlation phasor to
estimate the relative velocity. The technology is based on the correlation in
the wave-number space and it can provide more information than the normal
cross-correlation, such as the significant level (see Misra et al. 2010 for
detail). The shifts of Hα and sky emission lines are measured in the pixel
space to avoid the uncertainty of the wavelength calibration. The relative
shift of Hα to the sky line is 0.43$\pm$0.19 pixel, corresponding to
0.61$\pm$0.38 Å toward short wavelength. Then we used $\chi$-square
minimization to check the relative shift and the result is 0.42$\pm$0.36
pixel. No shift is found for Hα emission line at high significant level in the
two nights observations with a span of 4 nights.

###  2.2. Balmer absorption lines

Hα absorption features are detected in a specific region along the slit as
illustrated in Figure 1c. The regions with absorption features are located in
the same area of the sky but different parts of the CCD in two nights
observations, so these absorption features are unlikely artifacts of the CCD.
The same features were also found near Hβ, but the signal is too low to derive
reliable velocity.

In order to investigate the association between the Balmer absorption lines
and the environment of M81 ULS1, false-color maps were constructed in optical
(HST), UV (GALEX) and IR (Spitzer) wavelengths. Only the map of Spitzer
exhibits obvious structures along the slit (Figure 1a). The region with the
absorption features is correlated with the dark area in the map of 8 $\mu$m,
and the absorption features disappear when the 8 $\mu$m emission is strong.
Note that there is a positive correlation between 8 $\mu$m surface brightness
and Hα emission powered by star forming activity (Young et al., 2014). The
observed absorption features appear in the non-star-forming regions, and
likely come from the local stellar environments in the M81 bulge.

The absorption features in the background exhibit high velocities, $-$180
$\pm$ 25 km s-1at 52″ from the nucleus. The stellar kinematics studies (Vega
Beltrán et al., 2001) show that the bulge of M81 is rotating and the south-
east side is approaching with velocities around $-$180 km s-1at 45″ from the
nucleus. The similar velocity ($-$180 km s-1) suggests that the observed Hα
absorption lines come from stars in the bulge of M81. In comparison, the
systemic velocity of M81 is $-$34 km s-1(obtained from NED).

The Hα emission line ($-2\pm 23$ km s-1) exhibits a receding velocity of 178
$\pm$ 35 km s-1relative to the Hα absorption line from the stellar
environment. This suggests that ULS1 is not a system comoving with stars in
the bulge of M81. Such a relative velocity can come from an object in the halo
of M81 projected onto its bulge, which is receding along the line of sight.
Here we rule out the possibility that the system is located in the Milky Way,
since the column density of neutral hydrogen atoms at the location of ULS1 is
5.4 $\pm$ 0.5 $\times$ 1020 cm-2 in the Milky Way (Güver & Özel, 2009),
smaller than that derived from the X-ray spectrum fitting by a factor of 2
(Liu, 2008).

###  2.3. SED Construction

The spectral observations of M81 ULS1 enable us to obtain SED uncontaminated
by its intrinsic variabilities. Here we removed all the emission lines and
used a power-law function, $f_{\lambda}\propto\lambda^{\beta}$, to fit the
continuum of $\sim$ 4500$-$6000 Å, the high response wavelength range, in
order to estimate the spectral index on the blue side. The $\beta$ derived
from the best fitting is $-$2.36 $\pm$ 0.02, which corresponds to $\alpha\sim$
0.36 $\pm$ 0.02 for $f_{\nu}\propto\nu^{\alpha}$. The continuum on the blue
side is consistent with the $f_{\nu}\propto\nu^{1/3}$ relation expected for
the multi-color disk (MCD) model. On the red side, the spectrum of an AGB star
modeled with a temperature of 2277 K and a radius of 1301 R⊙, as suggested by
Liu & Di Stefano (2008), is presented in Figure 3. The combined SED is not
consistent with the red-side spectrum, which suggests that the AGB model may
be not a good explanation of the red component.

As a connection of X-ray and optical wavebands, the UV emission is important
to understand the SED of M81 ULS1. In order to probe the UV emission of M81
ULS1, we used the archive data of the Galaxy Evolution Explorer (GALEX; Martin
et al. 2005). M81 have been observed with three different tiles, and here we
used the tile with exposure time over 105 s to do accurate photometry. All the
sub-exposures of the tile were taken during the expected low state of M81 ULS1
(Liu, 2008; Wang et al., 2015). The photometric results are listed in Table 2
and also shown in Figure 3. Although with dispersions, the UV fluxes are lying
along the MCD powerlaw.

##  3\. Discussion

In this letter we report the first optical spectroscopic confirmation of an
accretion disk around a ULS. The broad Balmer emission lines of M81 ULS1
revealed by the Keck/LRIS observations are consistent with from an accretion
disk around a compact object. The spectral index of the continuum on the blue
side is also consistent with the $f_{\nu}\propto\nu^{1/3}$ relation expected
by MCD model. The velocity of the Hα emission line relative to its local
stellar environments suggests that M81 ULS1 is possibly a halo system in M81
belonging to an old population. Careful analysis shows no significant shift of
Hα emission line with a span of 4 days. This might suggest a small inclination
angle or a small velocity of the compact object. The latter can come from a
long orbital period, or a very massive compact object such as an IMBH.
However, the exact nature of the compact object is still unknown without
monitoring of the long-term motion of the system.

Besides emission lines of Balmer series, an emission line arises around 5530 Å
on the blue side with an FWHM of 1700 $\pm$ 200 km s-1, larger than that of Hα
by a factor of 4 (the lower panel in Figure 2). All exposures obtained in two
nights show the same emission feature and similar velocity dispersions, so it
is unlikely an artifact or cosmic rays. This emission line is too broad to be
a nebular line, and it is probably not an Fe II emission line because Fe+ ion
emits through a huge number of multiplets scattered across the blue side of
the spectrum (Baldwin et al., 2004; Shapovalova et al., 2012). Since the
relative shift is 10 Å $\pm$ 2 Å between two nights observations, the emission
line is unlikely related to the accretion disk. It is reported that N II
$\lambda$5530 and $\lambda$5535 emission lines have been detected for Ae/Be
stars (Mathew & Bhavya, 2010; Mathew & Subramaniam, 2011). However, no other
features are found to support the existence of an Ae/Be star, such as Fe II or
O I emission lines. More spectra with high signal-to-noise ratio are needed to
draw a conclusive result.

The spectrum of M81 ULS1 shows a gap between the red and blue sides due to the
low response on the edges of CCDs, and signal-to-noise ratio on the red side
is very low. Further deeper spectroscopic observations with the coverage of
$\sim$ 5000$-$9000 Å could present the complete spectrum with high signal-to-
noise ratio on the red side, which will enable us to characterize the
secondary.

The authors thank Prof. Bregman for his constructive comments on the
manuscript. Some of the data presented in this paper were obtained from the
Mikulski Archive for Space Telescopes (MAST). The authors acknowledge supports
from National Science Foundation of China under grants NSFC-11273028 and
NSFC-11333004, and support from National Astronomical Observatories, Chinese
Academy of Sciences under the Young Researcher Grant.

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