| 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 |
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