Papers by Aditya Sawant

2 papers
P-Stance: A Large Dataset for Stance Detection in Political Domain (2021.findings-acl)

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Challenge: stance detection is a method to determine whether a text author is in favor of, against or neutral toward a specific target.
Approach: They propose to use a large stance detection dataset in the political domain to detect stances on twitter.
Outcome: The proposed model achieves a macro-average F1-score of 80.53% and can be used to improve cross-domain stance detection.
NegBERT: A Transfer Learning Approach for Negation Detection and Scope Resolution (2020.lrec-1)

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Challenge: Negation is an important characteristic of language, and a major component of information extraction from text.
Approach: They propose to use a popular transfer learning model to solve Negation Detection and Scope Resolution tasks in 3 datasets that have gained popularity over the years.
Outcome: The proposed model outperforms existing systems on the BioScope Corpus, the Sherlock dataset and the SFU Review Corpus in scope resolution.

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