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Pushpak Bhattacharyya
Researcher at Indian Institute of Technology Patna
Publications - 576
Citations - 8724
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.
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Hindi and Marathi to English Cross Language Information Retrieval.
TL;DR: This paper presents the Hindi→English and Marathi→English CLIR systems developed as part of the participation in the CLEF 2007 Ad-Hoc Bilingual task, and takes a query translation based approach using bi-lingual dictionaries.
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M2H2: A Multimodal Multiparty Hindi Dataset For Humor Recognition in Conversations
Dushyant Singh Chauhan,Gopendra Vikram Singh,Navonil Majumder,Amir Zadeh,Asif Ekbal,Pushpak Bhattacharyya,Louis-Philippe Morency,Soujanya Poria +7 more
TL;DR: A dataset for Multimodal Multiparty Hindi Humor (M2H2) recognition in conversations containing 6,191 utterances from 13 episodes of a very popular TV series ”Shrimaan Shrimati Phir Se” is proposed and the empirical results demonstrate that multimodal information complements unimmodal information for humor recognition.
Automatic Generation of Multilingual Lexicon by Using Wordnet
TL;DR: This paper presents a method for automatically generating multilingual Universal Word (UW) dictionaries (for English, Hindi and Marathi) from an input document- making use of English, Sanskrit and Hindi WordNets.
Proceedings ArticleDOI
IITP-MT at CALCS2021: English to Hinglish Neural Machine Translation using Unsupervised Synthetic Code-Mixed Parallel Corpus
TL;DR: A neural machine translation (NMT) system which is trained on the synthetic code-mixed (cm) English-Hinglish parallel corpus and achieves 10.09 BLEU points over the given test set.
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Expect the unexpected: Harnessing Sentence Completion for Sarcasm Detection
TL;DR: This work considers an oracle case where the exact incongruous word is manually labeled in a corpus reported in past work, and sets up the promise for using sentence completion for sarcasm detection.