Structurally enriched entity mention embedding from semi-structured textual content

Structurally enriched entity mention embedding from semi-structured textual content

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-occurre...

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Bibliographic Details
Main Authors: HSIEH, Lee Hsun, LEE, Yang Yin, LIM, Ee-Peng
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2021
Subjects:
Entity mention embedding
structural enrichment
information extraction
lexical semantics
Databases and Information Systems
Numerical Analysis and Scientific Computing
Online Access:https://ink.library.smu.edu.sg/sis_research/5876
https://ink.library.smu.edu.sg/context/sis_research/article/6879/viewcontent/Structurally_Enriched_Entity_Mention_SAC2021_pv.pdf
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Institution: Singapore Management University
Language: English

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https://ink.library.smu.edu.sg/sis_research/5876
https://ink.library.smu.edu.sg/context/sis_research/article/6879/viewcontent/Structurally_Enriched_Entity_Mention_SAC2021_pv.pdf

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