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2021 | OriginalPaper | Buchkapitel

A Hierarchical Approach for Joint Extraction of Entities and Relations

verfasst von : Siqi Xiao, Qi Zhang, Jinquan Sun, Yu Wang, Lei Zhang

Erschienen in: Advances in Information Retrieval

Verlag: Springer International Publishing

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Abstract

Most existing approaches for the extraction of entities and relations face two main challenges: extracting overlapping relations and capturing the interactions between entity and relation extractions. In this paper, we present a novel sequence-to-sequence model with a hierarchical decoder to solve both issues elegantly and efficiently. Specifically, we use the low-level decoder to predict multi-relations and produce a relation vector for each triple. Given this relation vector, the high-level decoder generates two entities associated with the triple. In this manner, we can directly capture the interactions between entity and relation extractions. Moreover, by decomposing two tasks into two decoding phases, the overlapping multi-relations extraction can be naturally separated. Experiments on popular public datasets demonstrate that our model can effectively extract overlapping triples.

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Vorheriges Kapitel Consistency and Coherency Enhanced Story Generation
Nächstes Kapitel A Zero Attentive Relevance Matching Network for Review Modeling in Recommendation System
Fußnoten
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Metadaten
Titel
A Hierarchical Approach for Joint Extraction of Entities and Relations
verfasst von
Siqi Xiao
Qi Zhang
Jinquan Sun
Yu Wang
Lei Zhang
Copyright-Jahr
2021
Buch
Advances in Information Retrieval

Print ISBN: 978-3-030-72112-1

Electronic ISBN: 978-3-030-72113-8

Copyright-Jahr: 2021

DOI
https://doi.org/10.1007/978-3-030-72113-8_47

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