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''''''''?
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.
no code implementations • 16 Apr 2013 • Subhabrata Mukherjee, Pushpak Bhattacharyya
We will discuss in details various approaches to perform a computational treatment of sentiments and opinions.
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.
no code implementations • LREC 2014 • Mitesh M. Khapra, Ananthakrishnan Ramanathan, Anoop Kunchukuttan, Karthik Visweswariah, Pushpak Bhattacharyya
In contrast, we propose a low-cost QC mechanism which is fair to both workers and requesters.
no code implementations • WS 2015 • Ritesh Shah, Christian Boitet, Pushpak Bhattacharyya, Mithun Padmakumar, Leonardo Zilio, Ruslan Kalitvianski, Mohammad Nasiruddin, Mutsuko Tomokiyo, S P{\'a}ez, ra Castellanos
no code implementations • 10 Feb 2016 • Aditya Joshi, Pushpak Bhattacharyya, Mark James Carman
Automatic sarcasm detection is the task of predicting sarcasm in text.
no code implementations • 29 Mar 2016 • Geetanjali Rakshit, Sagar Sontakke, Pushpak Bhattacharyya, Gholamreza Haffari
In this paper, we attempt to solve the problem of Prepositional Phrase (PP) attachments in English.
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.
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.
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.
no code implementations • LREC 2016 • Shehzaad Dhuliawala, Diptesh Kanojia, Pushpak Bhattacharyya
We present a WordNet like structured resource for slang words and neologisms on the internet.
no code implementations • LREC 2016 • Md. Shad Akhtar, Asif Ekbal, Pushpak Bhattacharyya
Due to the phenomenal growth of online product reviews, sentiment analysis (SA) has gained huge attention, for example, by online service providers.
Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +4
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.
no code implementations • 1 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.
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.
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.
no code implementations • 4 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.
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.
no code implementations • 22 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.
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.
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
no code implementations • WS 2016 • Sukanta Sen, Debajyoty Banik, Asif Ekbal, Pushpak Bhattacharyya
Experiments show the BLEU of 13. 71 on the benchmark test data.
no code implementations • WS 2016 • Shweta Yadav, Asif Ekbal, Sriparna Saha, Pushpak Bhattacharyya
However, medical records enclose patient Private Health Information (PHI) which can reveal the identities of the patients.
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.
no code implementations • COLING 2016 • Md. Shad Akhtar, Ayush Kumar, Asif Ekbal, Pushpak Bhattacharyya
The sentiment augmented optimized vector obtained at the end is used for the training of SVM for sentiment classification.
no code implementations • COLING 2016 • Prerana Singhal, Pushpak Bhattacharyya
In this paper, we provide a solution to multilingual sentiment classification using deep learning.
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.
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.
1 code implementation • 1 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.
no code implementations • IJCNLP 2017 • Anoop Kunchukuttan, Maulik Shah, Pradyot Prakash, Pushpak Bhattacharyya
We investigate pivot-based translation between related languages in a low resource, phrase-based SMT setting.
no code implementations • 27 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.
no code implementations • 4 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.
no code implementations • 10 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.
no code implementations • EACL 2017 • Shweta Yadav, Asif Ekbal, Sriparna Saha, Pushpak Bhattacharyya
The proposed system is evaluated on three benchmark biomedical datasets such as GENIA, GENETAG, and AiMed.
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.
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.
no code implementations • 19 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).
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.
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.
no code implementations • SEMEVAL 2017 • Vikram Singh, Sunny Narayan, Md. Shad Akhtar, Asif Ekbal, Pushpak Bhattacharyya
This paper describes our system participation in the SemEval-2017 Task 8 {`}RumourEval: Determining rumour veracity and support for rumours{'}.
1 code implementation • SEMEVAL 2017 • N, Titas i, Chris Biemann, Seid Muhie Yimam, Deepak Gupta, Sarah Kohail, Asif Ekbal, Pushpak Bhattacharyya
In this paper we present the system for Answer Selection and Ranking in Community Question Answering, which we build as part of our participation in SemEval-2017 Task 3.
no code implementations • SEMEVAL 2017 • Abhishek Kumar, Abhishek Sethi, Md. Shad Akhtar, Asif Ekbal, Chris Biemann, Pushpak Bhattacharyya
The other system was based on Support Vector Regression using word embeddings, lexicon features, and PMI scores as features.
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.
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.
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.
no code implementations • WS 2017 • Md. Shad Akhtar, Palaash Sawant, Asif Ekbal, Jyoti Pawar, Pushpak Bhattacharyya
This paper describes the system that we submitted as part of our participation in the shared task on Emotion Intensity (EmoInt-2017).
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.
no code implementations • 6 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.
no code implementations • 16 Sep 2017 • Sreelekha. S, Pushpak Bhattacharyya
It is very unlikely for a parallel corpus to contain all morphological forms of words.
no code implementations • 5 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.
no code implementations • 5 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.
no code implementations • 5 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.
no code implementations • 5 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.
no code implementations • LREC 2018 • Anoop Kunchukuttan, Pratik Mehta, Pushpak Bhattacharyya
We present the IIT Bombay English-Hindi Parallel Corpus.
1 code implementation • 12 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.
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.
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.
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.
no code implementations • IJCNLP 2017 • Deepak Gupta, Pabitra Lenka, Harsimran Bedi, Asif Ekbal, Pushpak Bhattacharyya
Our empirical analysis shows that our models perform well in all the four languages on the setups of IJCNLP Shared Task on Customer Feedback Analysis.
1 code implementation • 14 Dec 2017 • Sachin Pawar, Girish K. Palshikar, Pushpak Bhattacharyya
In this paper, we survey several important supervised, semi-supervised and unsupervised RE techniques.
no code implementations • TACL 2018 • Anoop Kunchukuttan, Mitesh Khapra, Gurneet Singh, Pushpak Bhattacharyya
We address the task of joint training of transliteration models for multiple language pairs (multilingual transliteration).
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.
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).
no code implementations • WS 2018 • Nikhil Wani, S Mathias, eep, Jayashree Aan Gajjam, , Pushpak Bhattacharyya
In this paper, we present an effective system using voting ensemble classifiers to detect contextually complex words for non-native English speakers.
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.
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.
no code implementations • NAACL 2018 • Md. Shad Akhtar, Palaash Sawant, Sukanta Sen, Asif Ekbal, Pushpak Bhattacharyya
Efficient word representations play an important role in solving various problems related to Natural Language Processing (NLP), data mining, text mining etc.
Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +2
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.
no code implementations • WS 2018 • S Mathias, eep, Pushpak Bhattacharyya
Essays have two major components for scoring - content and style.
no code implementations • ACL 2018 • Sangameshwar Patil, Sachin Pawar, Swapnil Hingmire, Girish Palshikar, Vasudeva Varma, Pushpak Bhattacharyya
Identification of distinct and independent participants (entities of interest) in a narrative is an important task for many NLP applications.
1 code implementation • ACL 2018 • Rudra Murthy, Anoop Kunchukuttan, Pushpak Bhattacharyya
Multilingual learning for Neural Named Entity Recognition (NNER) involves jointly training a neural network for multiple languages.
no code implementations • 5 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.
no code implementations • 30 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.
1 code implementation • COLING 2018 • Tirthankar Ghosal, Vignesh Edithal, Asif Ekbal, Pushpak Bhattacharyya, George Tsatsaronis, Srinivasa Satya Sameer Kumar Chivukula
The proposed method outperforms the existing state-of-the-art on a document-level novelty detection dataset by a margin of ∼5{\%} in terms of accuracy.
no code implementations • COLING 2018 • Girishkumar Ponkiya, Kevin Patel, Pushpak Bhattacharyya, Girish Palshikar
It has been observed that uncovering the preposition is a significant step towards uncovering the predicate.
no code implementations • 3 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".
no code implementations • 5 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.
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).
1 code implementation • EMNLP 2018 • Deepanway Ghosal, Md. Shad Akhtar, Dushyant Chauhan, Soujanya Poria, Asif Ekbal, Pushpak Bhattacharyya
We evaluate our proposed approach on two multi-modal sentiment analysis benchmark datasets, viz.
Ranked #6 on Multimodal Sentiment Analysis on MOSI
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.
no code implementations • ACL 2018 • Sandeep Mathias, Diptesh Kanojia, Kevin Patel, Samarth Agarwal, Abhijit Mishra, Pushpak Bhattacharyya
Such subjective aspects are better handled using cognitive information.
no code implementations • 1 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.
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.
no code implementations • 12 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.
no code implementations • 24 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.
no code implementations • WS 2019 • Md. Shad Akhtar, Abhishek Kumar, Asif Ekbal, Chris Biemann, Pushpak Bhattacharyya
In this paper, we propose a language-agnostic deep neural network architecture for aspect-based sentiment analysis.
Aspect-Based Sentiment Analysis Aspect-Based Sentiment Analysis (ABSA) +3
no code implementations • NAACL 2019 • Md. Shad Akhtar, Dushyant Singh Chauhan, Deepanway Ghosal, Soujanya Poria, Asif Ekbal, Pushpak Bhattacharyya
In this paper, we present a deep multi-task learning framework that jointly performs sentiment and emotion analysis both.
no code implementations • WS 2019 • Abhijeet Dubey, Lakshya Kumar, Arpan Somani, Aditya Joshi, Pushpak Bhattacharyya
Initially, to get an insight into the problem, we implement a rule-based and a statistical machine learning-based (ML) classifier.
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.
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.
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{'}.
no code implementations • ACL 2019 • Hardik Chauhan, Mauajama Firdaus, Asif Ekbal, Pushpak Bhattacharyya
Multimodal dialogue systems have opened new frontiers in the traditional goal-oriented dialogue systems.
no code implementations • ACL 2019 • Shweta Yadav, Asif Ekbal, Sriparna Saha, Pushpak Bhattacharyya
The mining of adverse drug reaction (ADR) has a crucial role in the pharmacovigilance.
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.
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.
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.