arXiv subjects

Pushpak Bhattacharyya

Publications and source records attributed to Pushpak Bhattacharyya.

178 records · Page 10Linked to original sources

arXiv2017

SMPOST: Parts of Speech Tagger for Code-Mixed Indic Social Media Text

Use of social media has grown dramatically during the last few years. Users follow informal languages in communicating through social media. The language of communication is often mixed in nature, where people transcribe their regional language with English and this technique is found to be extremely popular. Natural language processing (NLP) aims to infer the information from these text where Part-of-Speech (PoS) tagging plays an important role in getting the prosody of the written text. For the task of PoS tagging on Code-Mixed Indian Social Media Text, we develop a supervised system based on Conditional Random Field classifier. In order to tackle the problem effectively, we have focused on extracting rich linguistic features. We participate in three different language pairs, ie. English-Hindi, English-Bengali and English-Telugu on three different social media platforms, Twitter, Facebook & WhatsApp. The proposed system is able to successfully assign coarse as well as fine-grained PoS tag labels for a given a code-mixed sentence. Experiments show that our system is quite generic that shows encouraging performance levels on all the three language pairs in all the domains.

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arXiv2017

Harnessing Cognitive Features for Sarcasm Detection

In this paper, we propose a novel mechanism for enriching the feature vector, for the task of sarcasm detection, with cognitive features extracted from eye-movement patterns of human readers. Sarcasm detection has been a challenging research problem, and its importance for NLP applications such as review summarization, dialog systems and sentiment analysis is well recognized. Sarcasm can often be traced to incongruity that becomes apparent as the full sentence unfolds. This presence of incongruity- implicit or explicit- affects the way readers eyes move through the text. We observe the difference in the behaviour of the eye, while reading sarcastic and non sarcastic sentences. Motivated by his observation, we augment traditional linguistic and stylistic features for sarcasm detection with the cognitive features obtained from readers eye movement data. We perform statistical classification using the enhanced feature set so obtained. The augmented cognitive features improve sarcasm detection by 3.7% (in terms of F-score), over the performance of the best reported system.

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arXiv2017

Leveraging Cognitive Features for Sentiment Analysis

Sentiments expressed in user-generated short text and sentences are nuanced by subtleties at lexical, syntactic, semantic and pragmatic levels. To address this, we propose to augment traditional features used for sentiment analysis and sarcasm detection, with cognitive features derived from the eye-movement patterns of readers. Statistical classification using our enhanced feature set improves the performance (F-score) of polarity detection by a maximum of 3.7% and 9.3% on two datasets, over the systems that use only traditional features. We perform feature significance analysis, and experiment on a held-out dataset, showing that cognitive features indeed empower sentiment analyzers to handle complex constructs.

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arXiv2016

`Who would have thought of that!': A Hierarchical Topic Model for Extraction of Sarcasm-prevalent Topics and Sarcasm Detection

Topic Models have been reported to be beneficial for aspect-based sentiment analysis. This paper reports a simple topic model for sarcasm detection, a first, to the best of our knowledge. Designed on the basis of the intuition that sarcastic tweets are likely to have a mixture of words of both sentiments as against tweets with literal sentiment (either positive or negative), our hierarchical topic model discovers sarcasm-prevalent topics and topic-level sentiment. Using a dataset of tweets labeled using hashtags, the model estimates topic-level, and sentiment-level distributions. Our evaluation shows that topics such as `work', `gun laws', `weather' are sarcasm-prevalent topics. Our model is also able to discover the mixture of sentiment-bearing words that exist in a text of a given sentiment-related label. Finally, we apply our model to predict sarcasm in tweets. We outperform two prior work based on statistical classifiers with specific features, by around 25\%.

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arXiv2016

Faster decoding for subword level Phrase-based SMT between related languages

A common and effective way to train translation systems between related languages is to consider sub-word level basic units. However, this increases the length of the sentences resulting in increased decoding time. The increase in length is also impacted by the specific choice of data format for representing the sentences as subwords. In a phrase-based SMT framework, we investigate different choices of decoder parameters as well as data format and their impact on decoding time and translation accuracy. We suggest best options for these settings that significantly improve decoding time with little impact on the translation accuracy.

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arXiv2016

A Computational Approach to Automatic Prediction of Drunk Texting

Alcohol abuse may lead to unsociable behavior such as crime, drunk driving, or privacy leaks. We introduce automatic drunk-texting prediction as the task of identifying whether a text was written when under the influence of alcohol. We experiment with tweets labeled using hashtags as distant supervision. Our classifiers use a set of N-gram and stylistic features to detect drunk tweets. Our observations present the first quantitative evidence that text contains signals that can be exploited to detect drunk-texting.

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arXiv2016

Are Word Embedding-based Features Useful for Sarcasm Detection?

This paper makes a simple increment to state-of-the-art in sarcasm detection research. Existing approaches are unable to capture subtle forms of context incongruity which lies at the heart of sarcasm. We explore if prior work can be enhanced using semantic similarity/discordance between word embeddings. We augment word embedding-based features to four feature sets reported in the past. We also experiment with four types of word embeddings. We observe an improvement in sarcasm detection, irrespective of the word embedding used or the original feature set to which our features are augmented. For example, this augmentation results in an improvement in F-score of around 4\% for three out of these four feature sets, and a minor degradation in case of the fourth, when Word2Vec embeddings are used. Finally, a comparison of the four embeddings shows that Word2Vec and dependency weight-based features outperform LSA and GloVe, in terms of their benefit to sarcasm detection.

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arXiv2016

Orthographic Syllable as basic unit for SMT between Related Languages

We explore the use of the orthographic syllable, a variable-length consonant-vowel sequence, as a basic unit of translation between related languages which use abugida or alphabetic scripts. We show that orthographic syllable level translation significantly outperforms models trained over other basic units (word, morpheme and character) when training over small parallel corpora.

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arXiv2016

Automatic Sarcasm Detection: A Survey

Automatic sarcasm detection is the task of predicting sarcasm in text. This is a crucial step to sentiment analysis, considering prevalence and challenges of sarcasm in sentiment-bearing text. Beginning with an approach that used speech-based features, sarcasm detection has witnessed great interest from the sentiment analysis community. This paper is the first known compilation of past work in automatic sarcasm detection. We observe three milestones in the research so far: semi-supervised pattern extraction to identify implicit sentiment, use of hashtag-based supervision, and use of context beyond target text. In this paper, we describe datasets, approaches, trends and issues in sarcasm detection. We also discuss representative performance values, shared tasks and pointers to future work, as given in prior works. In terms of resources that could be useful for understanding state-of-the-art, the survey presents several useful illustrations - most prominently, a table that summarizes past papers along different dimensions such as features, annotation techniques, data forms, etc.

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arXiv2016

Sharing Network Parameters for Crosslingual Named Entity Recognition

Most state of the art approaches for Named Entity Recognition rely on hand crafted features and annotated corpora. Recently Neural network based models have been proposed which do not require handcrafted features but still require annotated corpora. However, such annotated corpora may not be available for many languages. In this paper, we propose a neural network based model which allows sharing the decoder as well as word and character level parameters between two languages thereby allowing a resource fortunate language to aid a resource deprived language. Specifically, we focus on the case when limited annotated corpora is available in one language ($L_1$) and abundant annotated corpora is available in another language ($L_2$). Sharing the network architecture and parameters between $L_1$ and $L_2$ leads to improved performance in $L_1$. Further, our approach does not require any hand crafted features but instead directly learns meaningful feature representations from the training data itself. We experiment with 4 language pairs and show that indeed in a resource constrained setup (lesser annotated corpora), a model jointly trained with data from another language performs better than a model trained only on the limited corpora in one language.

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arXiv2016

Prepositional Attachment Disambiguation Using Bilingual Parsing and Alignments

In this paper, we attempt to solve the problem of Prepositional Phrase (PP) attachments in English. The motivation for the work comes from NLP applications like Machine Translation, for which, getting the correct attachment of prepositions is very crucial. The idea is to correct the PP-attachments for a sentence with the help of alignments from parallel data in another language. The novelty of our work lies in the formulation of the problem into a dual decomposition based algorithm that enforces agreement between the parse trees from two languages as a constraint. Experiments were performed on the English-Hindi language pair and the performance improved by 10% over the baseline, where the baseline is the attachment predicted by the MSTParser model trained for English.

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arXiv2013

Sentiment Analysis : A Literature Survey

Our day-to-day life has always been influenced by what people think. Ideas and opinions of others have always affected our own opinions. The explosion of Web 2.0 has led to increased activity in Podcasting, Blogging, Tagging, Contributing to RSS, Social Bookmarking, and Social Networking. As a result there has been an eruption of interest in people to mine these vast resources of data for opinions. Sentiment Analysis or Opinion Mining is the computational treatment of opinions, sentiments and subjectivity of text. In this report, we take a look at the various challenges and applications of Sentiment Analysis. We will discuss in details various approaches to perform a computational treatment of sentiments and opinions. Various supervised or data-driven techniques to SA like Naïve Byes, Maximum Entropy, SVM, and Voted Perceptrons will be discussed and their strengths and drawbacks will be touched upon. We will also see a new dimension of analyzing sentiments by Cognitive Psychology mainly through the work of Janyce Wiebe, where we will see ways to detect subjectivity, perspective in narrative and understanding the discourse structure. We will also study some specific topics in Sentiment Analysis and the contemporary works in those areas.

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arXiv2012

Leveraging Sentiment to Compute Word Similarity

In this paper, we introduce a new WordNet based similarity metric, SenSim, which incorporates sentiment content (i.e., degree of positive or negative sentiment) of the words being compared to measure the similarity between them. The proposed metric is based on the hypothesis that knowing the sentiment is beneficial in measuring the similarity. To verify this hypothesis, we measure and compare the annotator agreement for 2 annotation strategies: 1) sentiment information of a pair of words is considered while annotating and 2) sentiment information of a pair of words is not considered while annotating. Inter-annotator correlation scores show that the agreement is better when the two annotators consider sentiment information while assigning a similarity score to a pair of words. We use this hypothesis to measure the similarity between a pair of words. Specifically, we represent each word as a vector containing sentiment scores of all the content words in the WordNet gloss of the sense of that word. These sentiment scores are derived from a sentiment lexicon. We then measure the cosine similarity between the two vectors. We perform both intrinsic and extrinsic evaluation of SenSim and compare the performance with other widely usedWordNet similarity metrics.

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arXiv2012

TwiSent: A Multistage System for Analyzing Sentiment in Twitter

In this paper, we present TwiSent, a sentiment analysis system for Twitter. Based on the topic searched, TwiSent collects tweets pertaining to it and categorizes them into the different polarity classes positive, negative and objective. However, analyzing micro-blog posts have many inherent challenges compared to the other text genres. Through TwiSent, we address the problems of 1) Spams pertaining to sentiment analysis in Twitter, 2) Structural anomalies in the text in the form of incorrect spellings, nonstandard abbreviations, slangs etc., 3) Entity specificity in the context of the topic searched and 4) Pragmatics embedded in text. The system performance is evaluated on manually annotated gold standard data and on an automatically annotated tweet set based on hashtags. It is a common practise to show the efficacy of a supervised system on an automatically annotated dataset. However, we show that such a system achieves lesser classification accurcy when tested on generic twitter dataset. We also show that our system performs much better than an existing system.

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arXiv2012

Feature Specific Sentiment Analysis for Product Reviews

In this paper, we present a novel approach to identify feature specific expressions of opinion in product reviews with different features and mixed emotions. The objective is realized by identifying a set of potential features in the review and extracting opinion expressions about those features by exploiting their associations. Capitalizing on the view that more closely associated words come together to express an opinion about a certain feature, dependency parsing is used to identify relations between the opinion expressions. The system learns the set of significant relations to be used by dependency parsing and a threshold parameter which allows us to merge closely associated opinion expressions. The data requirement is minimal as this is a one time learning of the domain independent parameters. The associations are represented in the form of a graph which is partitioned to finally retrieve the opinion expression describing the user specified feature. We show that the system achieves a high accuracy across all domains and performs at par with state-of-the-art systems despite its data limitations.

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arXiv2012

WikiSent : Weakly Supervised Sentiment Analysis Through Extractive Summarization With Wikipedia

This paper describes a weakly supervised system for sentiment analysis in the movie review domain. The objective is to classify a movie review into a polarity class, positive or negative, based on those sentences bearing opinion on the movie alone. The irrelevant text, not directly related to the reviewer opinion on the movie, is left out of analysis. Wikipedia incorporates the world knowledge of movie-specific features in the system which is used to obtain an extractive summary of the review, consisting of the reviewer's opinions about the specific aspects of the movie. This filters out the concepts which are irrelevant or objective with respect to the given movie. The proposed system, WikiSent, does not require any labeled data for training. The only weak supervision arises out of the usage of resources like WordNet, Part-of-Speech Tagger and Sentiment Lexicons by virtue of their construction. WikiSent achieves a considerable accuracy improvement over the baseline and has a better or comparable accuracy to the existing semi-supervised and unsupervised systems in the domain, on the same dataset. We also perform a general movie review trend analysis using WikiSent to find the trend in movie-making and the public acceptance in terms of movie genre, year of release and polarity.

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