Towards Robust Named Entity Recognition via Temporal Domain Adaptation and Entity Context Understanding

被引：0

作者：

Agarwal, Oshin ^{[1
]}

机构：

[1] Univ Penn, Philadelphia, PA 19104 USA

来源：

THIRTY-SIXTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FOURTH CONFERENCE ON INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE / TWELVETH SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE | 2022年

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D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Named Entity Recognition models perform well on benchmark datasets but fail to generalize well even in the same domain. The goal of my thesis is to quantify the degree of indomain generalization in NER, probe models for entity name vs. context learning and finally improve their robustness, focusing on the recognition of ethnically diverse entities and new entities over time when the models are deployed.

引用

页码：12866 / 12867

页数：2