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Pushpak Bhattacharyya

Other affiliations: Xerox, IBM, Microsoft  ...read more
Bio: Pushpak Bhattacharyya is an academic researcher from Indian Institute of Technology Patna. The author has contributed to research in topics: Machine translation & WordNet. The author has an hindex of 38, co-authored 576 publications receiving 6465 citations. Previous affiliations of Pushpak Bhattacharyya include Xerox & IBM.


Papers
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Proceedings ArticleDOI
01 Jul 2015
TL;DR: A computational system that harnesses context incongruity as a basis for sarcasm detection is presented and it is shown how the features can capture intersentential incongrouity.
Abstract: The relationship between context incongruity and sarcasm has been studied in linguistics. We present a computational system that harnesses context incongruity as a basis for sarcasm detection. Our statistical sarcasm classifiers incorporate two kinds of incongruity features: explicit and implicit. We show the benefit of our incongruity features for two text forms tweets and discussion forum posts. Our system also outperforms two past works (with Fscore improvement of 10-20%). We also show how our features can capture intersentential incongruity.

285 citations

Journal ArticleDOI
TL;DR: Automatic sarcasm detection is the task of predicting sarcasm in text as mentioned in this paper, which is a crucial step to sentiment analysis, considering prevalence and challenges of sarcasm of sentiment-bearing text.
Abstract: 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, automatic sarcasm detection has witnessed great interest from the sentiment analysis community. This article is a 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 incorporation of context beyond target text. In this article, we describe datasets, approaches, trends, and issues in sarcasm detection. We also discuss representative performance values, describe shared tasks, and provide pointers to future work, as given in prior works. In terms of resources to understand the state-of-the-art, the survey presents several useful illustrations—most prominently, a table that summarizes past papers along different dimensions such as the types of features, annotation techniques, and datasets used.

277 citations

01 Jan 2010
TL;DR: This paper proposes in this paper a fall-back strategy to do sentiment analysis for Hindi documents, a problem on which, to the best of the knowledge, no work has been done until now.
Abstract: Sentiment Analysis (SA) research has gained tremendous momentum in recent times. However, there has been little work in this area for an Indian language. We propose in this paper a fall-back strategy to do sentiment analysis for Hindi documents, a problem on which, to the best of our knowledge, no work has been done until now. (A) First of all, we study three approaches to perform SA in Hindi. We have developed a sentiment annotated corpora in the Hindi movie review domain. The first of our approaches involves training a classifier on this annotated Hindi corpus and using it to classify a new Hindi document. (B) In the second approach, we translate the given document into English and use a classifier trained on standard English movie reviews to classify the document. (C) In the third approach, we develop a lexical resource called Hindi-SentiWordNet (H-SWN) and implement a majority score based strategy to classify the given document.

147 citations

Journal ArticleDOI
TL;DR: The work presented here is the only one to the authors' knowledge that describes language divergence phenomena in the framework of computational linguistics through a South Asian language.
Abstract: Interlingua and transfer-based approaches to machine translation have long been in use in competing and complementary ways. The former proves economical in situations where translation among multiple languages is involved, and can be used as a knowledge-representation scheme. But given a particular interlingua, its adoption depends on its ability (a) to capture the knowledge in texts precisely and accurately and (b) to handle cross-language divergences. This paper studies the language divergence between English and Hindi and its implication to machine translation between these languages using the Universal Networking Language (UNL). UNL has been introduced by the United Nations University, Tokyo, to facilitate the transfer and exchange of information over the internet. The representation works at the level of single sentences and defines a semantic net-like structure in which nodes are word concepts and arcs are semantic relations between these concepts. The language divergences between Hindi, an Indo-European language, and English can be considered as representing the divergences between the SOV and SVO classes of languages. The work presented here is the only one to our knowledge that describes language divergence phenomena in the framework of computational linguistics through a South Asian language.

143 citations

Journal ArticleDOI
TL;DR: A cascaded framework of feature selection and classifier ensemble using particle swarm optimization (PSO) for aspect based sentiment analysis using three classifiers, namely Maximum Entropy, Conditional Random Field and Support Vector Machine are presented.
Abstract: In this paper we present a cascaded framework of feature selection and classifier ensemble using particle swarm optimization (PSO) for aspect based sentiment analysis. Aspect based sentiment analysis is performed in two steps, viz. aspect term extraction and sentiment classification. The pruned, compact set of features performs better compared to the baseline model that makes use of the complete set of features for aspect term extraction and sentiment classification. We further construct an ensemble based on PSO, and put it in cascade after the feature selection module. We use the features that are identified based on the properties of different classifiers and domains. As base learning algorithms we use three classifiers, namely Maximum Entropy (ME), Conditional Random Field (CRF) and Support Vector Machine (SVM). Experiments for aspect term extraction and sentiment analysis on two different kinds of domains show the effectiveness of our proposed approach.

138 citations


Cited by
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Journal ArticleDOI
TL;DR: Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis.
Abstract: Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

13,246 citations

Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

01 Jan 2009

7,241 citations