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Fine-Grained Evaluation of Rule- and Embedding-Based Systems for Knowledge Graph Completion Read chapter Read first chapter

2018 | OriginalPaper | Chapter

QA4IE: A Question Answering Based Framework for Information Extraction

Authors : Lin Qiu, Hao Zhou, Yanru Qu, Weinan Zhang, Suoheng Li, Shu Rong, Dongyu Ru, Lihua Qian, Kewei Tu, Yong Yu

Published in: The Semantic Web – ISWC 2018

Publisher: Springer International Publishing

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Abstract

Information Extraction (IE) refers to automatically extracting structured relation tuples from unstructured texts. Common IE solutions, including Relation Extraction (RE) and open IE systems, can hardly handle cross-sentence tuples, and are severely restricted by limited relation types as well as informal relation specifications (e.g., free-text based relation tuples). In order to overcome these weaknesses, we propose a novel IE framework named QA4IE, which leverages the flexible question answering (QA) approaches to produce high quality relation triples across sentences. Based on the framework, we develop a large IE benchmark with high quality human evaluation. This benchmark contains 293K documents, 2M golden relation triples, and 636 relation types. We compare our system with some IE baselines on our benchmark and the results show that our system achieves great improvements.

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previous chapter Enriching Knowledge Bases with Counting Quantifiers
next chapter Constructing a Recipe Web from Historical Newspapers
Footnotes
1
Our source code and benchmark datasets can be found at https://​github.​com/​SJTU-lqiu/​QA4IE.
 
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Metadata
Title
QA4IE: A Question Answering Based Framework for Information Extraction
Authors
Lin Qiu
Hao Zhou
Yanru Qu
Weinan Zhang
Suoheng Li
Shu Rong
Dongyu Ru
Lihua Qian
Kewei Tu
Yong Yu
Copyright Year
2018
Book
The Semantic Web – ISWC 2018

Print ISBN: 978-3-030-00670-9

Electronic ISBN: 978-3-030-00671-6

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-030-00671-6_12

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