Pinaki Bhaskar


2021

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An On-device Deep-Learning Approach for Attribute Extraction from Heterogeneous Unstructured Text
Mahesh Gorijala | Aniruddha Bala | Pinaki Bhaskar | Krishnaditya . | Vikram Mupparthi
Proceedings of the 18th International Conference on Natural Language Processing (ICON)

Mobile devices, with their rapidly growing usage, have turned into rich sources of user information, holding critical insights for betterment of user experience and personalization. Creating, receiving and storing important information in the form of unstructured text has become a part and parcel of daily routine of users. From purchase deliveries in Short Message Service (SMS) or Notifications, to event booking details in Calendar applications, mobile devices serve as a portal for understanding user interests, behaviours and activities through information extraction. In this paper, we address the challenge of on-device extraction of user information from unstructured data in natural language from heterogeneous sources like messages, notification, calendar etc. The issue of privacy concern is effectively eliminated by the on-device nature of the proposed solution. Our proposed solution consists of 3 components – A Na ̈ıve-Bayes based classifier for domain identification, a Dual Character andWord based Bidirectional Long Short Term Memory (Bi-LSTM) and Conditional Random Field (CRF) model for attribute extraction and a rule-based Entity Linker. Our solution achieved a 93.29% F1 score on five domains (shopping, travel, event, service and personal). Since on-device deployment has memory and latency constraints, we ensure minimal model size and optimal inference latency. To demonstrate the efficacy of our approach, we have experimented on CoNLL- 2003 dataset and achieved comparable performance to existing benchmark results.

2013

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Answering Questions from Multiple Documents – the Role of Multi-Document Summarization
Pinaki Bhaskar
Proceedings of the Student Research Workshop associated with RANLP 2013

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Multi-Document Summarization using Automatic Key-Phrase Extraction
Pinaki Bhaskar
Proceedings of the Student Research Workshop associated with RANLP 2013

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Automatic Evaluation of Summary Using Textual Entailment
Pinaki Bhaskar | Partha Pakray
Proceedings of the Student Research Workshop associated with RANLP 2013

2012

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Keyphrase Extraction in Scientific Articles: A Supervised Approach
Pinaki Bhaskar | Kishorjit Nongmeikapam | Sivaji Bandyopadhyay
Proceedings of COLING 2012: Demonstration Papers

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Detection and Correction of Preposition and Determiner Errors in English: HOO 2012
Pinaki Bhaskar | Aniruddha Ghosh | Santanu Pal | Sivaji Bandyopadhyay
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP

2011

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May I check the English of your paper!!!
Pinaki Bhaskar | Aniruddha Ghosh | Santanu Pal | Sivaji Bandyopadhyay
Proceedings of the 13th European Workshop on Natural Language Generation

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A Rule Based Approach for Analysis of Comparative or Evaluative Questions in Tourism Domain
Bidhan Chandra Pal | Pinaki Bhaskar | Sivaji Bandyopadhyay
Proceedings of the KRAQ11 workshop

2010

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A Query Focused Multi Document Automatic Summarization
Pinaki Bhaskar | Sivaji Bandyopadhyay
Proceedings of the 24th Pacific Asia Conference on Language, Information and Computation