Contextualized End-to-End Neural Entity Linking

被引：0

作者：

Chen, Haotian ^{[1
]}

Zukov-Gregoric, Andrej ^{[1
]}

Li, Xi ^{[1
]}

Wadhwa, Sahil ^{[2
]}

机构：

[1] BlackRock, New York, NY 10055 USA

[2] Univ Illinois, Champaign, IL USA

来源：

1ST CONFERENCE OF THE ASIA-PACIFIC CHAPTER OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS AND THE 10TH INTERNATIONAL JOINT CONFERENCE ON NATURAL LANGUAGE PROCESSING (AACL-IJCNLP 2020) | 2020年

关键词：

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：

We propose an entity linking (EL) model that jointly learns mention detection (MD) and entity disambiguation (ED). Our model applies task-specific heads on top of shared BERT contextualized embeddings. We achieve stateof-the-art results across a standard EL dataset using our model; we also study our model's performance under the setting when hand-crafted entity candidate sets are not available and find that the model performs well under such a setting also.

引用

页码：637 / 642

页数：6

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