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2021 | OriginalPaper | Buchkapitel

An Argument Extraction Decoder in Open Information Extraction

verfasst von : Yucheng Li, Yan Yang, Qinmin Hu, Chengcai Chen, Liang He

Erschienen in: Advances in Information Retrieval

Verlag: Springer International Publishing

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Abstract

In this paper, we present a feature fusion decoder for argument extraction in Open Information Extraction (Open IE), where we challenge argument extraction as a predicate-dependent task. Therefore, we create a predicate-specific embedding layer to allow the argument extraction module fully shares the predicate information and the contextualized information of the given sentence, after using a pre-trained BERT model to achieve the predicates. After that, we propose a decoder in argument extraction that leverages both token features and span features to extract arguments with two steps as argument boundary identification by token features and argument role labeling by span features. Experimental results show that the proposed decoder significantly enhances the extraction performance. Our approach establishes a new state-of-the-art result on two benchmarks as OIE2016 and Re-OIE2016.

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Vorheriges Kapitel Answer Sentence Selection Using Local and Global Context in Transformer Models
Nächstes Kapitel Using the Hammer only on Nails: A Hybrid Method for Representation-Based Evidence Retrieval for Question Answering
Fußnoten
1
This subset is also used as test data in [18, 20].
 
2
The only difference is the confidence score for training data chosen by different baselines, please check Sect. 3.1 for details.
 
3
Note that results reported in [15] contradicts our results. That is because the author changed the matching function of evaluation scripts. While this changes the absolute performance numbers of the different systems, it does not change the relative performance of any of the tested systems.
 
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Metadaten
Titel
An Argument Extraction Decoder in Open Information Extraction
verfasst von
Yucheng Li
Yan Yang
Qinmin Hu
Chengcai Chen
Liang He
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_21

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