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2019 | OriginalPaper | Buchkapitel

An Effective BI-encoded Schema for Mention Extraction

verfasst von : Jerry Chun-Wei Lin, Jimmy Ming-Tai Wu, Yinan Shao, Matin Pirouz, Binbin Zhang

Erschienen in: Multidisciplinary Social Networks Research

Verlag: Springer Singapore

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Abstract

We present a neural-encoded mention-hypergraph (named as NEMH in this paper) model for mention-extraction and classification in this paper. Through extraction of textual mention entities, a model is proposed that applies a hypergraph-encoding schema to neural networks. Comparing the results of the proposed model with the previous approaches, the proposed model can thus identify unlimited-length nested mention entities, which is a major milestone in the field. Several experiments are conducted on many datasets used in the baseline approaches, and the obtained results indicated that the designed model has high effectiveness compared to the existing models.

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Titel: An Effective BI-encoded Schema for Mention Extraction
verfasst von: Jerry Chun-Wei Lin
Jimmy Ming-Tai Wu
Yinan Shao
Matin Pirouz
Binbin Zhang
Verlag: Springer Singapore
Buch: Multidisciplinary Social Networks Research
Print ISBN: 978-981-15-1757-0

Electronic ISBN: 978-981-15-1758-7

Copyright-Jahr: 2019
DOI: https://doi.org/10.1007/978-981-15-1758-7_5

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