Search Results for author: Pushpak Bhattacharyya

Found 366 papers, 38 papers with code

Cost and Benefit of Using WordNet Senses for Sentiment Analysis

no code implementations LREC 2012 Balamurali AR, Aditya Joshi, Pushpak Bhattacharyya

However, a moot question is ''''''``is the accuracy improvement commensurate with the cost incurred in annotation''''''''?

Sentiment Analysis

Experiences in Resource Generation for Machine Translation through Crowdsourcing

no code implementations LREC 2012 Anoop Kunchukuttan, Shourya Roy, Pratik Patel, Kushal Ladha, Somya Gupta, Mitesh M. Khapra, Pushpak Bhattacharyya

The logistics of collecting resources for Machine Translation (MT) has always been a cause of concern for some of the resource deprived languages of the world.

Machine Translation Translation

Sentiment Analysis : A Literature Survey

no code implementations16 Apr 2013 Subhabrata Mukherjee, Pushpak Bhattacharyya

We will discuss in details various approaches to perform a computational treatment of sentiments and opinions.

Opinion Mining Sentiment Analysis

Shata-Anuvadak: Tackling Multiway Translation of Indian Languages

no code implementations LREC 2014 Anoop Kunchukuttan, Abhijit Mishra, Rajen Chatterjee, Ritesh Shah, Pushpak Bhattacharyya

We present a compendium of 110 Statistical Machine Translation systems built from parallel corpora of 11 Indian languages belonging to both Indo-Aryan and Dravidian families.

Translation Transliteration

That'll Do Fine!: A Coarse Lexical Resource for English-Hindi MT, Using Polylingual Topic Models

no code implementations LREC 2016 Diptesh Kanojia, Aditya Joshi, Pushpak Bhattacharyya, Mark James Carman

As demonstrated by the quality of our coarse lexical resource and its benefit to MT, we believe that our sentential approach to create such a resource will help MT for resource-constrained languages.

Machine Translation Topic Models +1

Lexical Resources to Enrich English Malayalam Machine Translation

no code implementations LREC 2016 Sreelekha. S, Pushpak Bhattacharyya

We explore different ways of utilizing lexical resources to improve the quality of English Malayalam statistical machine translation.

Machine Translation Translation

Multiword Expressions Dataset for Indian Languages

no code implementations LREC 2016 Dhirendra Singh, Sudha Bhingardive, Pushpak Bhattacharyya

In this paper, we present MWEs annotation dataset created for Indian languages viz., Hindi and Marathi.

POS TAG +1

Synset Ranking of Hindi WordNet

no code implementations LREC 2016 Sudha Bhingardive, Rajita Shukla, Jaya Saraswati, Laxmi Kashyap, Dhirendra Singh, Pushpak Bhattacharyya

Various supervised, unsupervised and knowledge based approaches have been proposed for automatically determining the sense of a word in a particular context.

Information Retrieval Machine Translation +4

Sharing Network Parameters for Crosslingual Named Entity Recognition

no code implementations1 Jul 2016 Rudra Murthy V, Mitesh Khapra, Pushpak Bhattacharyya

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.

named-entity-recognition Named Entity Recognition +1

Orthographic Syllable as basic unit for SMT between Related Languages

no code implementations EMNLP 2016 Anoop Kunchukuttan, Pushpak Bhattacharyya

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.

Translation

Are Word Embedding-based Features Useful for Sarcasm Detection?

no code implementations EMNLP 2016 Aditya Joshi, Vaibhav Tripathi, Kevin Patel, Pushpak Bhattacharyya, Mark Carman

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.

Sarcasm Detection Semantic Similarity +2

A Computational Approach to Automatic Prediction of Drunk Texting

no code implementations4 Oct 2016 Aditya Joshi, Abhijit Mishra, Balamurali AR, Pushpak Bhattacharyya, Mark Carman

Alcohol abuse may lead to unsociable behavior such as crime, drunk driving, or privacy leaks.

Learning variable length units for SMT between related languages via Byte Pair Encoding

no code implementations WS 2017 Anoop Kunchukuttan, Pushpak Bhattacharyya

We explore the use of segments learnt using Byte Pair Encoding (referred to as BPE units) as basic units for statistical machine translation between related languages and compare it with orthographic syllables, which are currently the best performing basic units for this translation task.

Machine Translation Translation

Automatic Identification of Sarcasm Target: An Introductory Approach

no code implementations22 Oct 2016 Aditya Joshi, Pranav Goel, Pushpak Bhattacharyya, Mark Carman

To compare our approach, we use two baselines: a na\"ive baseline and another baseline based on work in sentiment target identification.

Sarcasm Detection Sentence

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

no code implementations WS 2016 Anoop Kunchukuttan, Pushpak Bhattacharyya

The increase in length is also impacted by the specific choice of data format for representing the sentences as subwords.

Translation

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

no code implementations WS 2016 Aditya Joshi, Prayas Jain, Pushpak Bhattacharyya, Mark Carman

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.

Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +2

IIT Bombay's English-Indonesian submission at WAT: Integrating Neural Language Models with SMT

no code implementations WS 2016 S. Singh, hya, Anoop Kunchukuttan, Pushpak Bhattacharyya

The Neural Probabilistic Language Model (NPLM) gave relatively high BLEU points for Indonesian to English translation system while the Neural Network Joint Model (NNJM) performed better for English to Indonesian direction of translation system.

Language Modelling Machine Translation +1

Leveraging Cognitive Features for Sentiment Analysis

no code implementations CONLL 2016 Abhijit Mishra, Diptesh Kanojia, Seema Nagar, Kuntal Dey, Pushpak Bhattacharyya

Sentiments expressed in user-generated short text and sentences are nuanced by subtleties at lexical, syntactic, semantic and pragmatic levels.

General Classification Sarcasm Detection +1

Harnessing Cognitive Features for Sarcasm Detection

no code implementations ACL 2016 Abhijit Mishra, Diptesh Kanojia, Seema Nagar, Kuntal Dey, Pushpak Bhattacharyya

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 Sentence +1

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

1 code implementation1 Feb 2017 Deepak Gupta, Shubham Tripathi, Asif Ekbal, Pushpak Bhattacharyya

For the task of PoS tagging on Code-Mixed Indian Social Media Text, we develop a supervised system based on Conditional Random Field classifier.

Part-Of-Speech Tagging POS +3

A case study on English-Malayalam Machine Translation

no code implementations27 Feb 2017 Sreelekha. S, Pushpak Bhattacharyya

In this paper we present our work on a case study on Statistical Machine Translation (SMT) and Rule based machine translation (RBMT) for translation from English to Malayalam and Malayalam to English.

Machine Translation Translation

Lexical Resources for Hindi Marathi MT

no code implementations4 Mar 2017 Sreelekha. S, Pushpak Bhattacharyya

In this paper we describe some ways to utilize various lexical resources to improve the quality of statistical machine translation system.

Machine Translation Translation

Comparison of SMT and RBMT; The Requirement of Hybridization for Marathi-Hindi MT

no code implementations10 Mar 2017 Sreelekha. S, Pushpak Bhattacharyya

We present in this paper our work on comparison between Statistical Machine Translation (SMT) and Rule-based machine translation for translation from Marathi to Hindi.

Machine Translation Translation

End-to-end Relation Extraction using Neural Networks and Markov Logic Networks

no code implementations EACL 2017 Sachin Pawar, Pushpak Bhattacharyya, Girish Palshikar

End-to-end relation extraction refers to identifying boundaries of entity mentions, entity types of these mentions and appropriate semantic relation for each pair of mentions.

General Classification Relation +2

Learning Cognitive Features from Gaze Data for Sentiment and Sarcasm Classification using Convolutional Neural Network

no code implementations ACL 2017 Abhijit Mishra, Kuntal Dey, Pushpak Bhattacharyya

We contend that manual extraction of features may not be the best way to tackle text subtleties that characteristically prevail in complex classification tasks like Sentiment Analysis and Sarcasm Detection, and that even the extraction and choice of features should be delegated to the learning system.

EEG Electroencephalogram (EEG) +4

Expect the unexpected: Harnessing Sentence Completion for Sarcasm Detection

no code implementations19 Jul 2017 Aditya Joshi, Samarth Agrawal, Pushpak Bhattacharyya, Mark Carman

However, since the exact word where such an incongruity occurs may not be known in advance, we present two approaches: an All-words approach (which consults sentence completion for every content word) and an Incongruous words-only approach (which consults sentence completion for the 50% most incongruous content words).

Sarcasm Detection Sentence +1

Adapting Pre-trained Word Embeddings For Use In Medical Coding

no code implementations WS 2017 Kevin Patel, Divya Patel, Mansi Golakiya, Pushpak Bhattacharyya, Nilesh Birari

We add information from medical coding data, as well as the first level from the hierarchy of ICD-10 medical code set to different pre-trained word embeddings.

Word Embeddings

Towards Harnessing Memory Networks for Coreference Resolution

no code implementations WS 2017 Joe Cheri, Pushpak Bhattacharyya

Coreference resolution task demands comprehending a discourse, especially for anaphoric mentions which require semantic information for resolving antecedents.

coreference-resolution Question Answering +1

IITP at SemEval-2017 Task 5: An Ensemble of Deep Learning and Feature Based Models for Financial Sentiment Analysis

no code implementations SEMEVAL 2017 Deepanway Ghosal, Shobhit Bhatnagar, Md. Shad Akhtar, Asif Ekbal, Pushpak Bhattacharyya

In this paper we propose an ensemble based model which combines state of the art deep learning sentiment analysis algorithms like Convolution Neural Network (CNN) and Long Short Term Memory (LSTM) along with feature based models to identify optimistic or pessimistic sentiments associated with companies and stocks in financial texts.

Sentiment Analysis

A Multilayer Perceptron based Ensemble Technique for Fine-grained Financial Sentiment Analysis

no code implementations EMNLP 2017 Md. Shad Akhtar, Abhishek Kumar, Deepanway Ghosal, Asif Ekbal, Pushpak Bhattacharyya

In this paper, we propose a novel method for combining deep learning and classical feature based models using a Multi-Layer Perceptron (MLP) network for financial sentiment analysis.

Sentiment Analysis Stock Prediction +1

Sentiment Intensity Ranking among Adjectives Using Sentiment Bearing Word Embeddings

no code implementations EMNLP 2017 Raksha Sharma, Arpan Somani, Lakshya Kumar, Pushpak Bhattacharyya

Identification of intensity ordering among polar (positive or negative) words which have the same semantics can lead to a fine-grained sentiment analysis.

Sentiment Analysis Word Embeddings

Computational Sarcasm

no code implementations EMNLP 2017 Pushpak Bhattacharyya, Aditya Joshi

In case of each of these algorithms, we refer to our work on sarcasm detection and share our learnings.

Sarcasm Detection Sentiment Analysis

"Having 2 hours to write a paper is fun!": Detecting Sarcasm in Numerical Portions of Text

no code implementations6 Sep 2017 Lakshya Kumar, Arpan Somani, Pushpak Bhattacharyya

We analyze the challenges of the problem, and present Rule-based, Machine Learning and Deep Learning approaches to detect sarcasm in numerical portions of text.

BIG-bench Machine Learning Sarcasm Detection +1

Phrase Pair Mappings for Hindi-English Statistical Machine Translation

no code implementations5 Oct 2017 Sreelekha. S, Pushpak Bhattacharyya

In this paper, we present our work on the creation of lexical resources for the Machine Translation between English and Hindi.

Machine Translation Translation

Bilingual Words and Phrase Mappings for Marathi and Hindi SMT

no code implementations5 Oct 2017 Sreelekha. S, Pushpak Bhattacharyya

Lack of proper linguistic resources is the major challenges faced by the Machine Translation system developments when dealing with the resource poor languages.

Machine Translation Translation

Morphology Generation for Statistical Machine Translation

no code implementations5 Oct 2017 Sreelekha. S, Pushpak Bhattacharyya

We use this method with the phrase-based and factor-based experiments on two morphologically rich languages: Hindi and Marathi when translating from English.

Machine Translation MORPH +1

Indowordnets help in Indian Language Machine Translation

no code implementations5 Oct 2017 Sreelekha. S, Pushpak Bhattacharyya

Then we have augmented the training corpus with Indowordnet synset word entries of lexical database and further trained 110 models on top of the baseline system.

Machine Translation Translation

Auto Analysis of Customer Feedback using CNN and GRU Network

1 code implementation12 Oct 2017 Deepak Gupta, Pabitra Lenka, Harsimran Bedi, Asif Ekbal, Pushpak Bhattacharyya

Analyzing customer feedback is the best way to channelize the data into new marketing strategies that benefit entrepreneurs as well as customers.

Marketing

Towards Lower Bounds on Number of Dimensions for Word Embeddings

no code implementations IJCNLP 2017 Kevin Patel, Pushpak Bhattacharyya

More specifically, we show that the number of pairwise equidistant words of the corpus vocabulary (as defined by some distance/similarity metric) gives a lower bound on the the number of dimensions , and going below this bound results in degradation of quality of learned word embeddings.

Named Entity Recognition (NER) Part-Of-Speech Tagging +4

Comparing Recurrent and Convolutional Architectures for English-Hindi Neural Machine Translation

no code implementations WS 2017 S. Singh, hya, Ritesh Panjwani, Anoop Kunchukuttan, Pushpak Bhattacharyya

In this paper, we empirically compare the two encoder-decoder neural machine translation architectures: convolutional sequence to sequence model (ConvS2S) and recurrent sequence to sequence model (RNNS2S) for English-Hindi language pair as part of IIT Bombay{'}s submission to WAT2017 shared task.

Image Captioning Language Modelling +4

Hindi Shabdamitra: A Wordnet based E-Learning Tool for Language Learning and Teaching

no code implementations WS 2017 Hanumant Redkar, S. Singh, hya, Meenakshi Somasundaram, Dhara Gorasia, Malhar Kulkarni, Pushpak Bhattacharyya

In today{'}s technology driven digital era, education domain is undergoing a transformation from traditional approaches to more learner controlled and flexible methods of learning.

Self-Learning

Relation Extraction : A Survey

1 code implementation14 Dec 2017 Sachin Pawar, Girish K. Palshikar, Pushpak Bhattacharyya

In this paper, we survey several important supervised, semi-supervised and unsupervised RE techniques.

Information Retrieval Management +6

TAP-DLND 1.0 : A Corpus for Document Level Novelty Detection

2 code implementations LREC 2018 Tirthankar Ghosal, Amitra Salam, Swati Tiwari, Asif Ekbal, Pushpak Bhattacharyya

Detecting novelty of an entire document is an Artificial Intelligence (AI) frontier problem that has widespread NLP applications, such as extractive document summarization, tracking development of news events, predicting impact of scholarly articles, etc.

Benchmarking Document Summarization +4

Meaningless yet meaningful: Morphology grounded subword-level NMT

no code implementations WS 2018 Tamali Banerjee, Pushpak Bhattacharyya

We explore the use of two independent subsystems Byte Pair Encoding (BPE) and Morfessor as basic units for subword-level neural machine translation (NMT).

NMT Segmentation +2

Multi-Task Learning Framework for Mining Crowd Intelligence towards Clinical Treatment

no code implementations NAACL 2018 Shweta Yadav, Asif Ekbal, Sriparna Saha, Pushpak Bhattacharyya, Amit Sheth

In this paper, we adopt a novel adversarial learning approach for our multi-task learning framework to learn the sentiment{'}s strengths expressed in a medical blog.

General Classification Multi-Task Learning +1

Fine-Grained Temporal Orientation and its Relationship with Psycho-Demographic Correlates

no code implementations NAACL 2018 Sabyasachi Kamila, Mohammed Hasanuzzaman, Asif Ekbal, Pushpak Bhattacharyya, Andy Way

In this paper, we propose a very first study to demonstrate the association between the sentiment view of the temporal orientation of the users and their different psycho-demographic attributes by analyzing their tweets.

Decision Making

Identifying Transferable Information Across Domains for Cross-domain Sentiment Classification

no code implementations ACL 2018 Raksha Sharma, Pushpak Bhattacharyya, D, S apat, ipan, Himanshu Sharad Bhatt

In this paper, we propose that words that do not change their polarity and significance represent the transferable (usable) information across domains for cross-domain sentiment classification.

Classification Domain Adaptation +5

Feature Assisted bi-directional LSTM Model for Protein-Protein Interaction Identification from Biomedical Texts

no code implementations5 Jul 2018 Shweta Yadav, Ankit Kumar, Asif Ekbal, Sriparna Saha, Pushpak Bhattacharyya

In this paper, we present a novel method based on deep bidirectional long short-term memory (B-LSTM) technique that exploits word sequences and dependency path related information to identify PPI information from text.

Leveraging Medical Sentiment to Understand Patients Health on Social Media

no code implementations30 Jul 2018 Shweta Yadav, Joy Sain, Amit Sheth, Asif Ekbal, Sriparna Saha, Pushpak Bhattacharyya

A large percentage of this population is actively engaged in health social networks to share health-related information.

A Multi-task Ensemble Framework for Emotion, Sentiment and Intensity Prediction

no code implementations3 Aug 2018 Md. Shad Akhtar, Deepanway Ghosal, Asif Ekbal, Pushpak Bhattacharyya, Sadao Kurohashi

In this paper, through multi-task ensemble framework we address three problems of emotion and sentiment analysis i. e. "emotion classification & intensity", "valence, arousal & dominance for emotion" and "valence & arousal} for sentiment".

Emotion Classification General Classification +1

Combining Graph-based Dependency Features with Convolutional Neural Network for Answer Triggering

no code implementations5 Aug 2018 Deepak Gupta, Sarah Kohail, Pushpak Bhattacharyya

Answer triggering is the task of selecting the best-suited answer for a given question from a set of candidate answers if exists.

Uncovering Code-Mixed Challenges: A Framework for Linguistically Driven Question Generation and Neural Based Question Answering

no code implementations CONLL 2018 Deepak Gupta, Pabitra Lenka, Asif Ekbal, Pushpak Bhattacharyya

In this paper, we propose a linguistically motivated technique for code-mixed question generation (CMQG) and a neural network based architecture for code-mixed question answering (CMQA).

Question Answering Question Generation +1

Is your Statement Purposeless? Predicting Computer Science Graduation Admission Acceptance based on Statement Of Purpose

no code implementations WS 2017 Diptesh Kanojia, Nikhil Wani, Pushpak Bhattacharyya

We present a quantitative, data-driven machine learning approach to mitigate the problem of unpredictability of Computer Science Graduate School Admissions.

Helping each Other: A Framework for Customer-to-Customer Suggestion Mining using a Semi-supervised Deep Neural Network

no code implementations1 Nov 2018 Hitesh Golchha, Deepak Gupta, Asif Ekbal, Pushpak Bhattacharyya

We evaluate the performance of our proposed model on a benchmark customer review dataset, comprising of the reviews of Hotel and Electronics domains.

Sentiment Analysis Suggestion mining

Addressing word-order Divergence in Multilingual Neural Machine Translation for extremely Low Resource Languages

no code implementations NAACL 2019 Rudra Murthy V, Anoop Kunchukuttan, Pushpak Bhattacharyya

To bridge this divergence, We propose to pre-order the assisting language sentence to match the word order of the source language and train the parent model.

Machine Translation NMT +3

A Deep Ensemble Framework for Fake News Detection and Classification

no code implementations12 Nov 2018 Arjun Roy, Kingshuk Basak, Asif Ekbal, Pushpak Bhattacharyya

Fake news, rumor, incorrect information, and misinformation detection are nowadays crucial issues as these might have serious consequences for our social fabrics.

Classification Fake News Detection +2

Relation extraction between the clinical entities based on the shortest dependency path based LSTM

no code implementations24 Mar 2019 Dhanachandra Ningthoujam, Shweta Yadav, Pushpak Bhattacharyya, Asif Ekbal

In this paper, we present an efficient relation extraction system based on the shortest dependency path (SDP) generated from the dependency parsed tree of the sentence.

Relation Relation Extraction +1

Extraction of Message Sequence Charts from Narrative History Text

no code implementations WS 2019 Girish Palshikar, Sachin Pawar, Sangameshwar Patil, Swapnil Hingmire, Nitin Ramrakhiyani, Harsimran Bedi, Pushpak Bhattacharyya, Vasudeva Varma

In this paper, we advocate the use of Message Sequence Chart (MSC) as a knowledge representation to capture and visualize multi-actor interactions and their temporal ordering.

Dependency Parsing

Extraction of Message Sequence Charts from Software Use-Case Descriptions

no code implementations NAACL 2019 Girish Palshikar, Nitin Ramrakhiyani, Sangameshwar Patil, Sachin Pawar, Swapnil Hingmire, Vasudeva Varma, Pushpak Bhattacharyya

We apply this tool to extract MSCs from several real-life software use-case descriptions and show that it performs better than the existing techniques.

Courteously Yours: Inducing courteous behavior in Customer Care responses using Reinforced Pointer Generator Network

no code implementations NAACL 2019 Hitesh Golchha, Mauajama Firdaus, Asif Ekbal, Pushpak Bhattacharyya

We use real interactions on Twitter between customer care professionals and aggrieved customers to create a large conversational dataset having both forms of agent responses: {`}generic{'} and {`}courteous{'}.

Multilingual Unsupervised NMT using Shared Encoder and Language-Specific Decoders

no code implementations ACL 2019 Sukanta Sen, Kamal Kumar Gupta, Asif Ekbal, Pushpak Bhattacharyya

In this paper, we propose a multilingual unsupervised NMT scheme which jointly trains multiple languages with a shared encoder and multiple decoders.

Denoising NMT +1

DeepSentiPeer: Harnessing Sentiment in Review Texts to Recommend Peer Review Decisions

1 code implementation ACL 2019 Tirthankar Ghosal, Rajeev Verma, Asif Ekbal, Pushpak Bhattacharyya

However, the peer review texts, which contains rich sentiment information of the reviewer, reflecting his/her overall attitude towards the research in the paper, could be a valuable entity to predict the acceptance or rejection of the manuscript under consideration.

Parallel Corpus Filtering Based on Fuzzy String Matching

no code implementations WS 2019 Sukanta Sen, Asif Ekbal, Pushpak Bhattacharyya

Based on the scores, we sub-sample two sets (having 1 million and 5 millions English tokens) of parallel sentences from each parallel corpus, and train SMT systems for development purpose only.

NMT Sentence

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