Search Results for author: Sachin Pawar

Found 22 papers, 1 papers with code

Weakly Supervised Extraction of Tasks from Text

no code implementations ICON 2021 Sachin Pawar, Girish Palshikar, Anindita Sinha Banerjee

In this paper, we propose a novel problem of automatic extraction of tasks from text.

Weak Supervision using Linguistic Knowledge for Information Extraction

no code implementations ICON 2020 Sachin Pawar, Girish Palshikar, Ankita Jain, Jyoti Bhat, Simi Johnson

The patterns in our patterns specification language are then matched on the ETF text rather than raw text to extract various entity mentions.

Techniques for Jointly Extracting Entities and Relations: A Survey

no code implementations10 Mar 2021 Sachin Pawar, Pushpak Bhattacharyya, Girish K. Palshikar

Traditionally, relation extraction is carried out after entity extraction in a "pipeline" fashion, so that relation extraction only focuses on determining whether any semantic relation exists between a pair of extracted entity mentions.

Relation Relation Extraction

Knowledge-based Extraction of Cause-Effect Relations from Biomedical Text

no code implementations10 Mar 2021 Sachin Pawar, Ravina More, Girish K. Palshikar, Pushpak Bhattacharyya, Vasudeva Varma

We propose a knowledge-based approach for extraction of Cause-Effect (CE) relations from biomedical text.

Extracting N-ary Cross-sentence Relations using Constrained Subsequence Kernel

no code implementations15 Jun 2020 Sachin Pawar, Pushpak Bhattacharyya, Girish K. Palshikar

Most of the past work in relation extraction deals with relations occurring within a sentence and having only two entity arguments.

Relation Relation Extraction +2

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.

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

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

End-to-End Relation Extraction using Markov Logic Networks

no code implementations4 Dec 2017 Sachin Pawar, Pushpak Bhattacharya, Girish K. Palshikar

Our end-to-end relation extraction performance is better than 2 out of 3 previous results reported on the ACE 2004 dataset.

Relation Relation Extraction +1

Mining Supervisor Evaluation and Peer Feedback in Performance Appraisals

no code implementations4 Dec 2017 Girish Keshav Palshikar, Sachin Pawar, Saheb Chourasia, Nitin Ramrakhiyani

All techniques are illustrated using a real-life dataset of supervisor assessment and peer feedback text produced during the PA of 4528 employees in a large multi-national IT company.

Clustering General Classification +4

Semi-Supervised Recurrent Neural Network for Adverse Drug Reaction Mention Extraction

no code implementations6 Sep 2017 Shashank Gupta, Sachin Pawar, Nitin Ramrakhiyani, Girish Palshikar, Vasudeva Varma

Current methods in ADR mention extraction relies on supervised learning methods, which suffers from labeled data scarcity problem.

Measuring Topic Coherence through Optimal Word Buckets

no code implementations EACL 2017 Nitin Ramrakhiyani, Sachin Pawar, Swapnil Hingmire, Girish Palshikar

Measuring topic quality is essential for scoring the learned topics and their subsequent use in Information Retrieval and Text classification.

General Classification Information Retrieval +4

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

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