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-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).