Anaphora Resolution from Social Media Text

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DC Field	Value	Language
dc.contributor.author	Sharma, Yashvardhan	-
dc.date.accessioned	2024-11-14T06:30:58Z	-
dc.date.available	2024-11-14T06:30:58Z	-
dc.date.issued	2022	-
dc.identifier.uri	https://ceur-ws.org/Vol-3395/T3-2.pdf	-
dc.identifier.uri	http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/16367	-
dc.description.abstract	Anaphora resolution for social media texts is essential yet difficult task for text understanding. An important characteristic of anaphora is that it creates a connection between the antecedent and the anaphor buried in the anaphoric sentence. This paper outlines the methods used to locate anaphora and their antecedents in a particular text. The text is a social media tweet for the SocAnaRes-IL 2022 challenge that was part of FIRE 2022. The proposed model uses a Neural Co-reference Network for the anaphora resolution	en_US
dc.language.iso	en	en_US
dc.publisher	CEUR-WS	en_US
dc.subject	Computer Science	en_US
dc.subject	Co-reference Resolution	en_US
dc.subject	Anaphora Resolution	en_US
dc.subject	Natural Language Processing (NLP)	en_US
dc.subject	Neural Co-reference	en_US
dc.title	Anaphora Resolution from Social Media Text	en_US
dc.type	Article	en_US
Appears in Collections:	Department of Computer Science and Information Systems

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