# Seed Word Selection for Weakly-Supervised Text Classification
with Unsupervised Error Estimation

Yiping Jin1,2, Akshay Bhatia2, Dittaya Wanvarie1
1Department of Mathematics & Computer Science, Chulalongkorn University,
Thailand
2Knorex, 140 Robinson Road, 14-16 Crown @ Robinson, Singapore
{jinyiping<EMAIL_ADDRESS>
<EMAIL_ADDRESS>

###### Abstract

Weakly-supervised text classification aims to induce text classifiers from
only a few user-provided seed words. The vast majority of previous work
assumes high-quality seed words are given. However, the expert-annotated seed
words are sometimes non-trivial to come up with. Furthermore, in the weakly-
supervised learning setting, we do not have any labeled document to measure
the seed words’ efficacy, making the seed word selection process “a walk in
the dark”. In this work, we remove the need for expert-curated seed words by
first mining (noisy) candidate seed words associated with the category names.
We then train interim models with individual candidate seed words. Lastly, we
estimate the interim models’ error rate in an unsupervised manner. The seed
words that yield the lowest estimated error rates are added to the final seed
word set. A comprehensive evaluation of six binary classification tasks on
four popular datasets demonstrates that the proposed method outperforms a
baseline using only category name seed words and obtained comparable
performance as a counterpart using expert-annotated seed words 111Source code
can be found at https://github.com/YipingNUS/OptimSeed..

##  1 Introduction

Weakly-supervised text classification eliminates the need for any labeled
document and induces classifiers with only a handful of carefully chosen seed
words. However, some researchers pointed out that the choice of seed words has
a significant impact on the performance of weakly-supervised models Li et al.
(2018); Jin et al. (2020). The vast majority of previous work assumed high-
quality seed words are given. However, many seed words reported in previous
work are not intuitive to come up with. For example, in Meng et al. Meng et
al. (2019), the seed words used for the category “Soccer” are {cup, champions,
united} instead of more intuitive keywords like “soccer” or “football”. We
conjecture the authors might have tried these more general keywords but
avoided them because they do not perform well.

While it is common to use labeled corpora to evaluate weakly-supervised text
classifiers in the literature, we do not have access to any labeled document
for new categories in the real-world setting. Therefore, there is no way to
measure the model’s performance and select the seed words that yield the best
accuracy. A similar concern on assessing active learning performance at
runtime has been raised by Kottke et al. Kottke et al. (2019).

In this work, we device $OptimSeed$, a novel framework to automatically
compose and select seed words for weakly-supervised text classification. We
firstly mine (noisy) candidate seed words associated with the category names.
We then train interim models with individual candidate seed words in an
iterative manner. Lastly, we use an unsupervised error estimation method to
estimate the interim models’ error rates. The keywords that yield the lowest
estimated error rates are selected as the final seed word set. A comprehensive
evaluation of six classification tasks on four popular datasets demonstrates
the effectiveness of the proposed method. The proposed method outperforms a
baseline using only the category name as seed word and obtained comparable
performance as a counterpart using expert-annotated seed words. We use binary
classification as a case study in this work, while the idea can be generalized
to multi-class classification using one-vs.-rest strategy.

The contributions of this work are three-fold:

  1. 1.

We propose a novel combination of unsupervised error estimation and weakly-
supervised text classification to improve the classification performance and
robustness.

  2. 2.

We conduct an in-depth study on the impact of different seed words on weakly-
supervised text classification, supported by experiments with various models
and classification tasks.

  3. 3.

The proposed method generates keyword sets that yield consistent and
competitive performance against expert-curated seed words.

##  2 Related Work

We review the literature in three related fields: (1) weakly-supervised text
classification, (2) unsupervised error estimation, and (3) keyword mining.

###  2.1 Weakly-Supervised Text Classification

Weakly-supervised text classification Druck et al. (2008); Meng et al. (2018,
2019) aims to use a handful of labeled seed words to induce text classifiers
instead of relying on labeled documents.

Druck et al. Druck et al. (2008) proposed generalized expectation (GE), which
specifies the expected posterior probability of labeled seed words appearing
in each category. GE is trained by optimizing towards satisfying the posterior
constraints without making use of pseudo-labeled documents.

Chang et al. Chang et al. (2008) introduced the first embedding based weakly-
supervised text classification method. They mapped category names and
documents into the same semantic space. Document classification is then
performed by searching for the nearest category embedding given an input
document.

Meng et al. Meng et al. (2018) proposed weakly-supervised neural text
classification. They generate unambiguous pseudo-documents, which are used to
induce text classifiers with different architectures such as convolutional
neural networks Kim (2014) or Hierarchical Attention Network Yang et al.
(2016).

Recently, Mekala and Shang Mekala and Shang (2020) disambiguate the seed words
by explicitly learning different senses of each word with contextualized word
embeddings. They first performed k-means clustering for each word in the
vocabulary to identify potentially different senses, then eliminated the
ambiguous keyword senses.

Two most recent works developed concurrently but independently from our work
Meng et al. (2020); Wang et al. (2020) addressed the same task we are
tackling: weakly-supervised text classification from only the category name.
They both tap on the category names’ contextualized representation and expand
the seed word list by finding other words that would fit into the same
context.

###  2.2 Unsupervised Error Estimation

Unsupervised error estimation aims to estimate the error rate of a list of
classifiers without a labeled evaluation dataset. It is widely relevant to
machine learning models in production, such as when a pre-trained model is
applied to a new domain or when labeled dataset is costly to obtain. To our
best knowledge, no previous work in weakly-supervised classification applied
unsupervised error estimation. Instead, they trained classifiers without
labeled training datasets but evaluated their models used labeled evaluation
datasets.

Most work in unsupervised error estimation derive the error rate analytically
by making simplifying assumptions. Donmez et al. Donmez et al. (2010) and
Jaffe et al. Jaffe et al. (2015) assumed the marginal probability of the
category $p(y)$ is known. Platanios et al. Platanios et al. (2014) assumed
classifiers make conditionally independent errors. While these approaches laid
an important theoretical foundation, most assumptions cannot be met for real-
world datasets and classifiers.

Platanios et al. Platanios et al. (2016) proposed a Bayesian approach for
error estimation. The model infers the true category and the error rates
jointly using Gibbs sampling. The approach was benchmarked with various
baselines such as majority vote and Platanios et al. Platanios et al. (2014)
and achieved superior performance. The estimated accuracy is usually within a
few percents from the true accuracy. Notably, the only mild assumption it
makes is that more than half of the classifiers have an error rate lower than
50%.

###  2.3 Keyword Mining

Keyword mining aims to bootstrap high-quality keyword lexicons from a small
set of seed words, and it has been widely used in mining opinion lexicons Hu
and Liu (2004); Hai et al. (2012) and technical glossaries Elhadad and Sutaria
(2007). We want to draw the association between keyword mining and weakly-
supervised text classification. Both tasks take a small list of seed words and
unlabeled corpus as input, aiming to “expand” the knowledge about the target
semantic category. Having more high-quality keywords will improve
classification accuracy, while an accurate classifier will make the keyword
mining task much easier by eliminating irrelevant/noisy documents.

##  3 Method

Figure 1 overviews OptimSeed, a framework to select seed words for weakly-
supervised text classification involving the following steps: (1) expanding
candidate keywords from a single seed word, (2) training interim classifiers
with individual candidate seed keywords using weakly supervision, (3) select
the final seed words with the feedback from unsupervised error estimation. We
discuss the proposed framework in detail in the following sections. To make
our paper self-contained, we also brief the weakly-supervised classification
and unsupervised error estimation model we use.

Figure 1: OptimSeed, a method to select seed words for weakly-supervised text
classification. We first mine noisy keywords associated with the category name
(the initial seed word). We use one iteration to refine the keywords for each
category. In each iteration, We fix the seed word for one category and combine
it with each mined keyword in the other category. The resultant keyword tuples
are used to train separate interim classifiers. Finally, we use Bayesian error
estimation to estimate the accuracy of classifiers induced from each keyword
tuple and select the keywords with the highest estimated accuracy.

###  3.1 Expanding Candidate Keywords from a Single Seed

We use either the category name or trivial keywords (e.g., “good” and “bad”
for sentiment classification tasks) as the only input seed word and use a
keyword expansion algorithm to mine more candidate keywords. We apply
$pmi$-$freq$ (Equation 1) following Jin et al. Jin et al. (2020). It is a
product of the logarithm of the candidate keyword $w$’s document frequency and
the point-wise mutual information between $w$ and the seed word $s$. The
higher the $pmi$-$freq$ score, the more strongly the candidate keyword is
associated with the seed word $s$. Additionally, we filter the mined keywords
based on their part-of-speech tag depending on the classification task. We
keep only noun candidates for topic classification and adjective candidates
for sentiment classification.

$pmi\mathchar 45\relax freq(w;s)\equiv log\,df(w)log\frac{p(w,s)}{p(w)p(s)}$
(1)

###  3.2 Training interim classifiers

The candidate keywords and unlabeled dataset are used to induce interim
classifiers. Interim classifiers’ purpose is to isolate the impact of
individual seed words so that we can rank them. Specifically, iteration A in
Figure 1 tries to rank candidate seed words for Category A (Movies) in the
classification task Movies-Television. The initial seed word “television” for
Category B is fixed, and it forms seed word tuples with each candidate word in
Category A. We use each such seed word tuple as input to train an interim
classifier. We then use each interim classifier’s predictions to perform
unsupervised error estimation.

We use Generalized Expectation (GE) Druck et al. (2008) to train both interim
classifiers and the final classifier because of its competitive performance
and fast training speed 222All GE models in this work can be trained within a
few seconds using a single CPU core.. GE translates labeled keywords to
constraint functions. For example, the first keyword tuple (hollywood,
television) in Figure 1 translates to two constraint functions:
$hollywood\rightarrow{A:0.9,B:0.1}$ and $television\rightarrow{A:0.1,B:0.9}$,
which means “hollywood” is expected to occur 90% in a document of category A
while 10% in a document of category B, vice versa for the keyword
“television”.

Each constraint function on a labeled word $w_{k}$ contributes to a term in
the objective function in Equation 2 and the underlying logistic regression
model is trained by minimizing the L2 distance between the reference
distribution $\hat{p}(y|w_{k}>0)$ (specified by the constraint function) and
the empirical distribution $\tilde{p}(y|w_{k}>0)$ (predicted by the model) of
the category $y$ when word $w_{k}$ is present.

$\mathcal{O}=-\sum_{k\in K}dist(\hat{p}(y|w_{k}>0)||\tilde{p}(y|w_{k}>0))$ (2)

###  3.3 Keyword Evaluation with Bayesian Error Estimation

We apply unsupervised error estimation on the interim classifiers’ predictions
to estimate their accuracy and select the best seed words for the final
classifier. As shown in Figure 1 iteration A, the three keywords “hollywood”,
“filmmaker”, and “theaters” are added to the final seed word set of Category A
(Movies) because their corresponding interim classifiers have estimated
accuracy above the threshold. The process is repeated in iteration B to select
seed words for Category B.

We use the Bayesian error estimation (BEE) model Platanios et al. (2016) to
perform this step. In BEE, each instance’s true label is latent, while each
model’s predictions are observed. The accuracy/error rate can be derived from
the predictions and the latent true labels. The assumption that half of the
classifiers have an error rate below 50% implicitly uses inter-classifier
agreement.

BEE uses Gibbs sampling to infer the error rates of individual classifiers
$e_{j}$ and the true label $l_{i}$ jointly. We refer the readers to Section
4.1 in Platanios et al. Platanios et al. (2016) for the exact conditional
probabilities used in Gibbs sampling.

##  4 Experimental Setup

We use six binary classification tasks from four datasets to evaluate our
framework. We choose the evaluation tasks so that they cover different
granularities and domains. The details are as follows:

  * •

AG’s News Dataset: contains 120,000 documents evenly distributed into 4 coarse
categories. We randomly choose two binary classification tasks: “Politics” vs.
“Technology” and “Business” vs. “Sports”.

  * •

The New York Times (NYT) Dataset: contains 13,081 news articles covering 5
coarse and 25 fine-grained categories. We choose two fine-grained binary
classification tasks involving categories with similar semantics:
“International Business” (InterBiz) vs. “Economy” and “Movies” vs.
“Television”.

  * •

Yelp Restaurant Review Dataset: contains 38,000 reviews evenly distributed
into 2 categories: “Positive” vs. “Negative”.

  * •

IMDB Movie Review Dataset: contains 50,000 reviews evenly distributed into 2
categories: “Positive” vs. “Negative”.

We report the performance of the following weakly-supervised models besides
Generalized Expectation (GE):

  * •

Dataless Chang et al. (2008): maps both input documents and category seed
words into a semantic space using Explicit Semantic Analysis (ESA) Gabrilovich
et al. (2007) over Wikipedia concepts and assigns the category nearest to the
input document’s embedding.

  * •

MNB/Priors Settles (2011): increases priors for labeled keywords in a Naïve
Bayes model and learns from an unlabeled corpus using EM algorithm.

  * •

WESTCLASS Meng et al. (2018): weakly-supervised neural text classifier trained
using pseudo documents. We use the CNN architecture because Meng et al. Meng
et al. (2018) showed that it outperformed other architectures such as RNNs and
Hierarchical Attention Network.

  * •

ConWea Mekala and Shang (2020): leverages contextualized word representations
to differentiate multiple senses. It also trains classifiers and expands seed
words in an iterative manner.

We also report the performance of LR, a supervised logistic regression model
trained using all the documents in the training set 333We use the logistic
regression implementation in scikit-learn with default parameters and tf-idf
features..

In all experiments, we mine 16 candidate seed words with the highest
$pmi$-$freq$ score for each category. We select a candidate keyword for the
final classifier if its estimated accuracy is among the top three or is higher
than 0.9 444Mekala and Shang Mekala and Shang (2020) observed that three seed
words per class are needed for reasonable performance while more high-quality
keywords help. Therefore, we use the accuracy threshold of 0.9 to include
additional keywords.. For GE, we use a reference distribution of 0.9 (meaning
a labeled keyword is expected to appear in its specified categories 90% of the
time) following Druck et al. Druck et al. (2008). Table 1 shows the initial
seed words used in our work and in previous work 555The seed words for NYT
corpus were reported in Meng et al. Meng et al. (2019) and the rest are from
Meng et al. Meng et al. (2018). No previous work in weakly supervision used
IMDB dataset, so we use the same manual seed words as Yelp dataset..

Class |  Our Work |  Previous Work
---|---|---
Politics

Tech |  political;

technology |  democracy religion liberal;
scientists biological computing
Business

Sports |  business;

sports |  economy industry
investment;
hockey tennis basketball
InterBiz
Economy |  international;
economy |  china union euro;
fed economists economist
Movies

Television |  movie;

television |  hollywood directed oscar;
episode viewers episodes
Yelp & IMDB |  good;

bad |  terrific great
awesome;
horrible subpar
disappointing
Table 1: Initial seed words for each task.

##  5 Classification Performance

Table 2 presents each model’s average accuracy across six datasets.

Method |  cate |  ours |  gold
---|---|---|---
Dataless |  54.7 |  60.4∗ |  56.7
MNB/Priors |  68.5 |  71.7 |  74.4
WESTCLASS |  75.7 |  77.2 |  77.0
ConWea |  60.0 |  66.0 |  70.7
GE |  80.4 |  84.8∗ |  85.1
LR | 91.8
Table 2: Average accuracy scores in percentage for all methods on all six
classification tasks. cate, ours, gold indicates the result using the category
name, keywords selected by OptimSeed and expert-composed keywords used in
previous work. For each model, the best-performing keyword set is highlighted
in bold. ∗ indicates statistical significance from the same model using “cate”
seed word with p-value of 0.05 using paired t-test.

We can see that OptimSeed seed words yield better performance than using the
category name alone by a large margin for all weakly-supervised models,
validating the effectiveness of our seed word expansion and selection method.
It also achieved better or similar performance as expert-curated seed words
for three out of five models.

Among the learning algorithms, GE obtained the best average performance for
all seed word sets. The average accuracy of GE using OptimSeed seed words
(84.8%) is only 0.3% lower than using expert-curated seed words, virtually
eliminating human experts from the loop. GE+OptimSeed’s accuracy is 7% below a
fully-supervised logistic regression model trained on hundreds to tens of
thousands of labeled documents.

Table 3 shows each model’s classification accuracy on topic classification
tasks. Summing over all models and datasets, OptimSeed achieved better or
equal performance than the category name baseline 80% of the time (16/20) and
better or equal performance than the gold seed words 65% of the time. It
demonstrates the robustness of our seed word selection method across different
tasks.

While ConWea claimed to resolve ambiguity through contextualized embeddings,
we observed that it works well only when the input seed words are unambiguous
(“ours” or “gold” column). On the Business-Sports classification task, its
accuracy was only 39.1% while other baselines could achieve over 90%. We
inspected the model and found the keywords expanded by ConWea are much noisier
than OptimSeed, which caused the poor performance.

Method | Poli-Tech | Biz-Sport | IB-Econ | Movie-TV
---|---|---|---|---
cate |  ours |  gold |  cate |  ours |  gold |  cate |  ours |  gold |  cate |  ours |  gold
Dataless |  50.1 |  51.4 |  50.2 |  50.0 |  50.2 |  50.4 |  59.1 |  75.0 |  67.1 |  67.8 |  70.0 |  67.8
MNB/Priors |  87.3 |  88.9 |  88.9 |  95.6 |  93.9 |  92.9 |  58.5 |  54.3 |  93.9 |  67.8 |  67.8 |  68.9
WESTCLASS |  87.4 |  89.5 |  88.8 |  92.7 |  94.8 |  94.3 |  77.7 |  83.0 |  75.1 |  50.4 |  76.6 |  62.1
ConWea |  71.5 |  73.7 |  71.4 |  39.1 |  67.0 |  82.0 |  75.1 |  71.2 |  84.3 |  66.9 |  77.0 |  76.4
GE |  86.9 |  87.8 |  88.5 |  93.0 |  93.0 |  79.4 |  70.7 |  81.7 |  91.5 |  94.4 |  98.9 |  97.8
LR | 96.3 | 98.6 | 90.2 | 85.5
Table 3: Accuracy on topic classification tasks. For each model-dataset
combination, we highlight the best performance in bold.

We can make similar observations on the performance of sentiment
classification tasks (Table 4). However, the gap between weakly-supervised
models and the supervised baseline is much larger topic classification tasks,
suggesting that some reviews’ sentiment might be expressed implicitly and
requires more than word-level understanding. Meng et al. (2020) also made a
similar remark based on their experiment.

Method | Yelp
---|---
cate |  ours |  gold
Dataless |  51.0 |  55.5 |  52.2
MNB/Priors |  50.9 |  71.5 |  51.7
WESTCLASS |  78.3 |  58.8 |  81.5
ConWea |  51.0 |  51.3 |  50.7
GE |  68.0 |  75.2 |  79.3
LR | 92.2

Method | IMDB
---|---
cate |  ours |  gold
Dataless |  50.1 |  60.4 |  52.2
MNB/Priors |  51.1 |  54.0 |  50.3
WESTCLASS |  67.7 |  60.6 |  60.5
ConWea |  56.5 |  55.7 |  59.1
GE |  69.6 |  72.2 |  74.0
LR | 88.3

Table 4: Accuracy on sentiment classification tasks. For each model-dataset
combination, we highlight the best performance in bold.

##  6 Case Study

To demonstrate the working of our proposed framework, we present a case study
on the classification task “International Business” vs. “Economy” in Table 5
and show different seed word sets for the category “economy” and their
corresponding performance.

Keyword expansion alone improved the accuracy significantly from the category
name baseline. However, it may introduce some ambiguous keywords in the
meantime. The unsupervised error estimator successfully identified top
keywords such as “economist” and “economists” and eliminated poor keywords
like “purchases” and “growth”, which further improved the accuracy by 2.4%.

Stage:Acc |  Seed Words for “Economy”
---|---
Init: 70.7 |  economy
Keyword Expansion: 79.3 |  purchases pace index
borrowing unemployment
economists economy stimulus
rates recovery economist rate
fed reserve inflation growth
Final: 81.7 |  economist economists rate
recovery index
Table 5: Seed words for “Economy” at different stages of the OptimSeed
framework.

##  7 Conclusion

Weakly-supervised text classification can induce classifiers with a handful of
carefully-chosen seed words instead of labeled documents. However, the choice
of seed words has a significant impact on classification performance. We
proposed $OptimSeed$, a novel framework to compose the seed words
automatically. It first mines keywords associated with the category name and
then estimates each seed word’s impact directly using unsupervised error
estimation. The framework outputs seed words yielding a comparable performance
as expert-curated seed words, virtually eliminating human experts from the
loop.

##  8 Acknowledgements

YJ was supported by the scholarship from ‘The 100th Anniversary Chulalongkorn
University Fund for Doctoral Scholarship’. We thank anonymous reviewers for
their valuable feedback.

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Finnish Centre for Astronomy with ESO (FINCA), University of Turku,
Väisäläntie 20, FI-21500 Piikkiö, Finland A.J<EMAIL_ADDRESS>
† In memoriam: This paper is dedicated to the memory of our late colleague
Simon Clark who inspired us all to try to understand the stars.

# 4XMM J182531.5$-$144036: A new persistent Be/X-ray binary found within the
_XMM-Newton_ serendipitous survey

A.B. Mason  School of Physical Sciences, The Open University, Walton Hall,
Milton Keynes MK7 6AA, UK  A.J. Norton  School of Physical Sciences, The Open
University, Walton Hall, Milton Keynes MK7 6AA, UK  J.S. Clark† School of
Physical Sciences, The Open University, Walton Hall, Milton Keynes MK7 6AA, UK
S.A. Farrell  Department of Physics & Astronomy, University of Leicester,
Leicester LE1 7RH, UK  A.J. Gosling  Astrophysics, Department of Physics,
University of Oxford, Denys Wilkinson Building, Keble Road, Oxford OX1 3RH, UK
[

###### Abstract

We aim to investigate the nature of time-variable X-ray sources detected in
the XMM-Newton serendipitous survey. The X-ray light curves of objects in the
XMM-Newton serendipitous survey were searched for variability and coincident
serendipitous sources observed by Chandra were also investigated. Subsequent
infrared spectroscopy of the counterparts to the X-ray objects that were
identified using UKIDSS was carried out using ISAAC on the VLT. We found that
the object 4XMM J182531.5–144036 detected in the XMM-Newton serendipitous
survey in April 2008 was also detected by Chandra as CXOU J182531.4–144036 in
July 2004. Both observations reveal a hard X-ray source displaying a coherent
X-ray pulsation at a period of 781 s. The source position is coincident with a
$K=14$ mag infrared object whose spectrum exhibits strong HeI and Br$\gamma$
emission lines and an infrared excess above that of early B-type dwarf or
giant stars. We conclude that 4XMM J182531.5–144036 is a Be/X-ray binary
pulsar exhibiting persistent X-ray emission and is likely in a long period,
low eccentricity orbit, similar to X Per.

###### keywords:

stars: emission-line, Be; stars: neutron; stars: binaries: general

##  1 Introduction

Be/X-ray binaries (BeXRBs) comprise the largest subset of the High Mass X-ray
Binaries HMXRBcat (3). They consist of an X-ray emitting neutron star pulsar
in an (often eccentric) orbit around a Be star, i.e. a non-supergiant B-type
star that has shown hydrogen lines in emission. Be stars also show an excess
of infrared emission, compared to a star of the same spectral type. The
emission lines and infrared excess are attributed to an equatorial
circumstellar disc, due to material expelled from the rapidly rotating Be
star. Some BeXRBs exhibit persistent X-ray emission whilst others only show
such emission close to periastron, when the neutron star passes through the Be
star circumstellar disc Reig11 (13).

Here we present the discovery of a new BeXRB, 4XMM J182531.5$-$144036,
identified from an investigation of hard X-ray sources in the XMM-Newton
serendipitous survey. The source was also detected by Chandra so we
investigate those archival observations too. In Section we introduce the X-ray
observations and discuss the reduction of both data sets. The timing and
spectral analysis of the X-ray source is then described within Sections 3 and
4 respectively. Section 5 examines near infrared observations and the
identification of the counterpart to this system and Section 6 presents a
discussion of our results.

##  2 X-ray observations

###  2.1 XMM-Newton

The field of 4XMM J182531.5$-$144036 was observed on the 10th April, 2008 by
XMM-Newton jansen01 (6) for $33.5$ ks starting at MJD 54566.76197. The target
was the Wolf-Rayet star WR 115 and the source 4XMM J182531.5$-$144036 is
located close to the edge of the field of view (see Figure 1, left). The
observation was performed using all three EPIC cameras in imaging mode; the
MOS1, MOS2 and PN cameras were employed in full frame mode using a medium
filter. Additionally the Optical Monitor (OM) instrument performed optical/UV
observations with the UVM2 filter.

Figure 1: Left: Merged XMM-Newton EPIC RGB mosaic image (Red: 0.2–1 keV,
Green: 1–2 keV, Blue: 2–10 keV). Gaussian smoothing with a kernel radius of 3
has been applied to the image. 4XMM J182531.5$-$144036 is the ringed light
blue source near the centre. Right: Chandra ACIS-I merged RGB mosaic image
(Red: 0.5–1.2 keV, Green: 1.2–2.0 keV and Blue: 2.0–7.0 keV. CXOU
J182531.4$-$144036 is the ringed source at the top of the right hand ACIS-I
detector. Featured prominently on the left-hand side of the detector is the
HMXB V479 Sct/LS 5039 hadasch12 (5).

4XMM J182531.5$-$144036 was initially detected by the XMM-Newton pipeline
watson09 (17) at RA: $18^{\text{h}}$ $25^{\text{m}}$ $31.5^{\text{s}}$ and
Dec: $-14^{\circ}$ $40^{\prime}$ $36.6^{\prime\prime}$ (J2000) with a
positional uncertainty of $1.1^{\prime\prime}$ ($3\sigma$). For this
observation the 2MASS catalogue was used to correct the source position,
providing a reduced pointing error of $0.35^{\prime\prime}$. No source was
detected in the OM mosaiced image within the EPIC error circle.

Observation Data Files (ODFs) were processed using the XMM-Newton Science
Analysis System (SAS) v21.0 (http://xmm.esa.int/sas/). Event lists for each PN
and MOS camera were generated using the epproc or emproc script respectively,
each employing the most recent calibration files, and using default
parameters. Bad events caused by cosmic rays, edge effects and bad rows were
identified and discarded. Single event light curves were constructed for all
three EPIC cameras using energies greater than 10 keV to detect any episodes
of soft proton flaring that may have occurred during the observation. For each
camera a Good Time Interval (GTI) file was created to filter out these flaring
episodes, with a background threshold of 1 count s-1 for the PN and 0.3 count
s-1 for the MOS detectors. This resulted in net exposure times after flare
removal of 28.0/32.8/32.9 ks for the PN/MOS1/MOS2 cameras respectively.

Source events were extracted for all three detectors in the energy range
$0.2-10$ keV using an extraction region of radius $44^{\prime\prime}$
surrounding 4XMM J182531.5$-$144036\. Background events were also extracted
from an identical sized circular region (free of any contaminating sources)
and located at the same off-axis position. For the EPIC-PN spectrum and light
curve we selected single pixel and double pixel events; for EPIC-MOS we used
single to quadruple events. The source had EPIC PN /MOS1/MOS2 count rates of
$0.177\pm 0.003$ / $0.056\pm 0.002$ / $0.060\pm 0.002$ counts s-1 respectively
(background subtracted and corrected for vignetting and the point spread
function). Images in three distinct energy bands, 0.2 – 1 keV (Red), 1 – 2 keV
(Green) and 2 – 10 keV (Blue) were extracted from the data and merged into a
RGB mosaic (Figure 1) to display the spectral colours. The size of the 2-d
bins used in making the images was 100 pixels per bin in both $x$ and $y$
direction (about 5 arcsec).

Source and background light curves with the maximum time resolution of 73 ms
for the PN and 2.6 s for MOS1 and MOS2 were then created. To improve
statistics we re-binned the PN light curve to a resolution of 2.6 s and
combined this with the MOS1 and MOS2 light curves into a single light curve.
The GTIs for each camera largely overlap with each other, and all GTIs for
each camera were used in the subsequent analysis. After background subtraction
the resultant light curve was barycentrically corrected using the task
barycen. Background and source spectra were then extracted for each EPIC
camera, together with response and ancillary response files. The spectra were
not re-binned, and the full spectral resolution was retained for the
subsequent spectral fitting.

###  2.2 Chandra

The Chandra source designated CXOU J182531.4$-$144036 was detected during an
observation of the supernova remnant G16.85$-$1.05 (PI: Garmire) on the 11th
July, 2004 for $\sim 18$ ks starting at MJD 53197.51016. The observations
employed the Advanced CCD Imaging Spectrometer (ACIS-I) garmire03b (4) in
wide-field imaging mode utilising chips I0 – I3 plus S2 and S3 in faint data
mode. The data was reduced using CIAO v4.5 and the latest calibration files at
that time (calDB 4.5.7). Initially we reprocessed the default level 2 files
using the chandra$\\_$repro script to apply the current calibration set. All
processing of the data was carried out with reference to the list of current
Chandra data reduction threads (http://cxc.harvard.edu/ciao/threads/).

Selecting a circular background region with a radius of 42′′ from the Level 2
event file, we created a light curve in the energy range 0.5 – 7.0 keV to
ascertain the degree of flaring. No periods of flaring were detected during
the full exposure time. A three colour image of field was then constructed
(see Figure 1, right). To convert the counts detected into fluxes, we then
constructed an exposure map in the energy range 0.3 – 8 keV with a centre-band
energy of 2.3 keV.

To extract events in the energy range 0.2 – 8 keV from CXOU J182531.4$-$144036
we used the ChaRT and MARX programs to calculate the radius given by 90$\%$ of
the PSF at an energy of 4.5 keV. The calculated PSF is heavily dependent upon
the off-axis and azimuthal angles. As the source is close to the edge of the
detector, this yielded an off-axis angle $\theta=8.3^{\prime}$ and azimuthal
angle $\phi=341.186^{\circ}$, giving an extraction radius of $\sim
10^{\prime\prime}$ for a region enclosed by 90% of the PSF. For the background
extraction regions we used a circular radius of $40^{\prime\prime}$ at a
similar off-axis and azimuthal angle as the source. The script specextract was
used to extract source and background spectra and generate the response files.
Prior to analysis these spectra were again binned at 20 counts per bin to
improve statistics. Using the script dmextract we created a background
subtracted lightcurve with a maximum time resolution of 3.2s, before
barycentrically correcting this lightcurve using the tool axbary.

Figure 2: Left: Chandra background subtracted light curve binned at 150 s. The
time axis begins at the start of the observation, at 53197.510 MJD. Right:
Power spectrum of the Chandra background subtracted light curve, showing the
detection of modulation at a period of 781 s. The $5\sigma$ signal detection
threshold is indicated by the dashed line.

##  3 Timing analysis

###  3.1 Chandra

Figure 2 displays the background subtracted lightcurve of CXOU
J182531.4$-$144036\. It is immediately apparent from this lightcurve that this
source exhibits significant variability. The source was previously discussed
by muno08 (11) who detected a 780 s periodic signal in the same archival
Chandra data, when searching for magnetars.

To investigate this further, a power spectrum (Figure 2) was generated for the
3.2 s resolution ACIS-I lightcurve using the Xronos tool powspec. Upon
examination it is apparent that there is a prominent peak that occurs at $\sim
780$ s and a weaker harmonic at half this period. The $5\sigma$ period
detection threshold is indicated on the plot. To further constrain the value
of the period of this source, we employed an epoch-folding period search
method larsson96 (8). Using the HEASOFT task efsearch we searched for periods
between 400 – 1200 s. The resolution of the period search is defined by the
Fourier Period Resolution (FPR) parameter $\delta P=P^{2}/2T_{\text{obs}}$.
With $P=780$ s and $T_{\text{obs}}=18$ ks, this gives $\delta P\sim 16.9$ s.
To improve the likelihood of finding the best period we overestimated the FPR
by a factor of $\sim 20$ giving $\delta P=0.85$ s. We then used this reduced
$\delta P$ as the period resolution of the epoch folding period search. The
plot of $\chi^{2}$ versus period produced by the efsearch task shows a broad
peak and fitting this with a Gaussian yielded a central value of $781$ s. The
uncertainty in the Gaussian central value is $\sim 0.15$ s, so the 1-$\sigma$
confidence level in the period is estimated as this uncertainty multiplied by
the FPR overestimation factor, yielding $\pm 3$ s. This period of $781\pm 3$ s
is in agreement with that found by muno08 (11). The 0.2 – 8 keV light curve
was then phase folded in 10 phase bins over this best-fitted period and the
resulting folded light curve is shown in Figure 3.

Figure 3: Chandra background subtracted light curve folded over the best fit
period of 781 s. The zero phase is set at the point of minimum observed
modulation. The error bars represent the 1-$\sigma$ confidence level.

###  3.2 XMM-Newton

The background subtracted and combined EPIC light curve of 4XMM
J182531.5$-$144036 is shown in Figure 4 where it has been binned at 150 s for
clarity. As with the Chandra light curve, it is apparent from Figure 4 that
this source displays a noticeable degree of variability. This combined light
curve was used to construct a power spectrum, in order to determine the nature
of any periodic signal from the source. Figure 4 shows the power spectrum
obtained and once again the $5\sigma$ period detection threshold is indicated.
The power spectrum obtained is very similar to that from Chandra in exhibiting
a significant peak at $\sim 780$ s.

Using efsearch we searched for a period in the same time frame as that of the
Chandra source, namely between $400-1200$ s. Again, fitting the peak value
found from the folded period search with a Gaussian, a central value of 781 s
was obtained. The 1-$\sigma$ uncertainty in the period was also obtained using
the method described in Section 3.1. The best fitting period was found to be
$781\pm 2$ s. Figure 5 shows the EPIC 0.2 – 10 keV light curve phase folded at
this period.

Figure 4: Left: EPIC (PN, MOS1 and MOS2 combined) 0.2 – 10.0 keV background
subtracted light curve binned at 150 s. The time axis indicates the time since
the start of the observation at MJD 54566.761. Gaps in the light curve are the
result of removing background flares. Right: Power spectrum of the EPIC
background subtracted light curve. Below 1000 s the most prominent peak in the
spectrum occurs at $\sim 780$ s. The $5\sigma$ signal detection threshold is
indicated by the dashed line. Figure 5: The EPIC 0.2 – 10 keV background
subtracted light curve folded over the best fit period. The zero phase is set
at the point of minimum observed modulation. 1-$\sigma$ error bars are shown.

##  4 X-ray Spectral analysis

We extracted energy spectra for 4XMM J182531.5$-$144036 for all three EPIC
cameras in the XMM-Newton observation and the ACIS-I camera from the Chandra
observation. The spectral analysis was performed with XSPEC v.12.8.0 arnaud96
(1). The four spectra were fitted simultaneously in the energy range 0.4 –
10.0 keV with an absorbed power-law, with the hydrogen column density
$N_{\text{H}}$, photon index $\Gamma$ and normalisation left as free
parameters. Fitting was performed using the C-stat statistic kaastra17 (7)
which is appropriate for spectra with few counts per channel. The absorbed
power-law produced an acceptable fit and is shown in Figure 6. The best-fit
parameters are $N_{\text{H}}=(6.9\pm 0.5)\times
10^{22}~{}\text{atoms~{}cm}^{-2}$ and $\Gamma=2.2\pm 0.2$. There is no
evidence for an iron line at 6.4 keV. The 0.2 – 10.0 keV unabsorbed flux from
the fit is $F_{X}=(7.2\pm 0.1)\times 10^{-12}$ erg s-1 cm2. For the regretably
unconstrained limits in distance for the source (derived from NIR photometry
and the spectral classification of the counterpart, see Section 5.1) of $\sim
1$ kpc and $\sim 7$ kpc, this corresponds to X-ray luminosities of $8\times
10^{32}$ erg s-1 and $4\times 10^{34}$ erg s-1 respectively. We also attempted
to fit the spectrum with an exponentially cutoff power law model, which is
often seen in Be/X-ray binaries. However this does not improve the fit and the
resulting parameter uncertainties are even less well constrained than for the
simple power law model.

Figure 6: The combined XMM-Newton EPIC and Chandra ACIS-I spectrum of 4XMM
J182531.5$-$144036\. (top) The EPIC-PN (black), EPIC-MOS1 (red), EPIC-MOS2
(green) and ACIS-I (blue) data points together with the fitted absorbed power
law model. (bottom) The residuals between the data and the best-fit model.

##  5 Near infrared observations

###  5.1 UKIDSS photometry

Figure 7: A $25^{\prime\prime}\times 38^{\prime\prime}$ UKIDSS J-band finding
chart for 4XMM J182531.5$-$144036\. The red circle is centred on the XMM-
Newton detected position, with a radius of $1^{\prime\prime}$ equal to the
positional error. The white circle is centred on the Chandra detected position
and has a radius of $0.6^{\prime\prime}$ equal to its positional error.

To search for any possible near infrared (NIR) counterparts to the X-ray
source 4XMM J182531.5$-$144036 we employed the UKIDSS (UKIRT Infrared Deep Sky
Survey) Galactic Plane Survey DR6 photometric data warren07 (16) to search a
region of 6′′ surrounding its position. There was only one candidate with
UKIDSS UGPS J182531.48–144036.5 located at 0.6′′ from the reported X-ray
position. It has infrared magnitudes of $J=16.28$, $H=14.92$ and $K=13.99$
respectively.

Figure 7 shows a J-band UKIDSS image of the region surrounding the position of
4XMM J182531.5$-$144036\. The white circle represents the Chandra detected
position with a 95$\%$ confidence limit for CXOU J182531.4-144036. The red
circle describes the 1-$\sigma$ positional uncertainty of the source as
detected by XMM-Newton. The NIR counterpart is labelled as object C.

The X-ray spectral fitting gave an equivalent hydrogen column density to the
source of $N_{\text{H}}=6.9\times 10^{22}$ atoms cm-2. Assuming this to be due
to interstellar material, using the relationship of Predehl (12), this implies
an optical extinction of $A_{V}=38.5$ mag, or in the K-band $A_{K}=4.3$ mag
Rieke (15). Therefore the de-reddened K-band magnitude of the counterpart may
be estimated as $K=9.7$ mag. For an assumed spectral type of O9 – B3 III – V,
the absolute K-band magnitude is in the range $-4.48$ to $-0.25$ BlumConti (2)
MartinsPlez (9). So the distance may be estimated as between $\sim 1$ kpc and
$\sim 7$ kpc. The object is too faint to be detected by _Gaia_ so we have no
independent distance estimate.

Finally, the UKIDSS JHK magnitudes of the counterpart are plotted in Figure 8
along with Kurucz model atmosphere spectral energy distributions of O9V, B1V
and B3III stars in the range 1.0 $\mu$m to 2.2 $\mu$m. These are normalised to
the J-band magnitude of the counterpart, and show that the infrared spectrum
of the candidate at wavelengths longer than 1.5 $\mu$m is far brighter than
these comparison stellar types.

Figure 8: The spectral energy distribution (SED) in the JHK region of the
infrared counterpart to 4XMM J182531.5$-$144036, shown by the three asterisks.
Models for the SEDs of O9V, B1V and B3III stars are shown for comparison.

###  5.2 ISAAC spectrum

The NIR candidate was subsequently observed with the NIR spectrograph ISAAC on
the VLT on 14th May 2011 as part of a programme of follow-up infrared spectra
of counterparts to 24 X-ray sources. Due to the faintness of the targets we
utilised the SW LRes mode to obtain low-resolution ($R\sim 500$) spectra in
the Ks band using a $0.8^{\prime\prime}$ wide slit. Nodded science spectra
were obtained at a central wavelength of 2.2 $\mu$m for an integration time of
2160 s, followed by observations of the bright (Ks = 8.5) B8V telluric
standard Hip 092285 for an integration time of 10 s.

The $120^{\prime\prime}$ long slit was orientated $130^{\circ}$ anti-clockwise
from North. As such two further stars, marked A and B in Figure 7 with
magnitudes J = 13.23 and J = 16.07 respectively, were also within the slit.
The automatic ISAAC pipeline reduction process defaults to extracting the
brightest spectrum in the slit, which in this case was star A (whose spectrum
included multiple CO bandheads indicative of a red giant). The spectrum of the
candidate counterpart C was subsequently extracted manually, and is shown in
Figure 9. It displays strong emission lines corresponding to HeI (2.06 $\mu$m)
and hydrogen Br$\gamma$ (2.165 $\mu$m). The equivalent width of the Br$\gamma$
line is $7.0\pm 1.3$ Å.

Figure 9: NIR spectrum of the counterpart to 4XMM J182531.5$-$144036 obtained
with ISAAC on the VLT.

##  6 Discussion

The infrared counterpart to 4XMM J182531.5$-$144036 displays a near-infrared
excess when compared to the spectra of early B-type dwarf or giant stars and
exhibits a strong hydrogen emission line. These features are characteristic of
the Be star phenomenon. Furthermore, the presence of a coherent X-ray
pulsation with a period of 781 s in both the XMM-Newton and Chandra data of
4XMM J182531.5$-$144036 is typical of that seen in BeXRB pulsars Reig11 (13)
while the asymmetric nature of the X-ray pulse profile is also typical of
accreting X-ray pulsars and can provide information about the magnetic field
structure of the neutron star meszaros84 (10). The fact that the X-ray
pulsation is seen with the same profile in widely separated observations (XMM-
Newton from April 2008 and Chandra from July 2004) indicates that the X-ray
emission is likely persistent. The hard X-ray spectrum, lack of an iron line,
and (poorly constrained) inferred luminosity are also consistent with other
wide-orbit, low eccentricity BeXRBs with relatively slow neutron star spin
periods ($>200$ s), such as X Per ReigRoche (14). Considering the locus of
BeXRB systems on the Corbet diagram Reig11 (13), we may estimate that this
object has an orbital period in the range $\sim 250-500$ d. We therefore
conclude that 4XMM J182531.5$-$144036 is a newly identified persistent, long
period, Be/X-ray binary.

This work is based on data obtained as part of the UKIRT Infrared Deep Sky
Survey and on observations collected at the European Organisation for
Astronomical Research in the Southern Hemisphere under ESO programme
087.D-0873(A). It also uses observations obtained with _XMM-Newton_ , an ESA
science mission with instruments and contributions directly funded by ESA
Member States and NASA, and data obtained from the _Chandra_ Data Archive and
the _Chandra_ Source Catalog, with software provided by the _Chandra_ X-ray
Center (CXC) in the application packages CIAO and Sherpa. We thank the
anonymous referee for several useful suggestions.

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