Structurally enriched entity mention embedding from semi-structured textual content

• 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
• Institution: Singapore Management University

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...