Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

3-2021

Abstract

In this research, we propose a novel and effective entity mention embedding framework that learns from semi-structured text corpus with annotated entity mentions without the aid of well-constructed knowledge graph or external semantic information other than the corpus itself. Based on the co-occurrence of words and entity mentions, we enrich the co-occurrence matrix with entity-entity, entity-word, and word-entity relationships as well as the simple structures within the documents. Experimentally, we show that our proposed entity mention embedding benefits from the structural information in link prediction task measured by mean reciprocal rank (MRR) and mean precision@K (MP@K) on two datasets for Named-entity recognition (NER).

Keywords

Entity mention embedding, structural enrichment, information extraction, lexical semantics

Discipline

Databases and Information Systems | Numerical Analysis and Scientific Computing

Research Areas

Data Science and Engineering

Publication

SAC ’21: Proceedings of the 36th ACM/SIGAPP Symposium On Applied Computing, March 22–26, 2021, Virtual Event, Republic of Korea

First Page

1

Last Page

4

ISBN

978145038104

Identifier

10.1145/3412841.3442100

Publisher

ACM

City or Country

New York

Embargo Period

3-25-2021

Copyright Owner and License

LARC and Authors

Additional URL

https://doi.org/10.1145/3412841.3442100

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