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2018 | OriginalPaper | Buchkapitel

Metadata Extraction for Scientific Papers

verfasst von : Binjie Meng, Lei Hou, Erhong Yang, Juanzi Li

Erschienen in: Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data

Verlag: Springer International Publishing

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Abstract

Metadata extraction for scientific literature is to automatically annotate each paper with metadata that represents its most valuable information, including problem, method and dataset. Most existing work normally extract keywords or key phrases as concepts for further analysis without their fine-grained types. In this paper, we present a supervised method with three-stages to address the problem. The first step extracts key phrases as metadata candidates, and the second step introduces various features, i.e., statistical features, linguistics features, position features and a novel fine-grained distribution feature which has high relevance with metadata categories, to type the candidates into three foregoing categories. In the evaluation, we conduct extensive experiments on a manually-labeled dataset from ACL Anthology and the results show our proposed method achieves a +3.2% improvement in accuracy compared with strong baseline methods.

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Vorheriges Kapitel Research on Chinese-Tibetan Neural Machine Translation

Nächstes Kapitel Knowledge Graph Embedding with Logical Consistency

https://en.wikipedia.org/wiki/Metadata.

https://github.com/shangjingbo1226/SegPhrase.

http://aclweb.org/anthology/.

http://grobid.readthedocs.io/en/latest/.

The metadata of papers in the Dublin Core include: Title, Creator, Subject, Description, Contributor, Publisher, Date, Type, Format, Identifier, Source, Relation, Reference, Is referenced By, Language, Rights and Coverage.

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Titel: Metadata Extraction for Scientific Papers
verfasst von: Binjie Meng
Lei Hou
Erhong Yang
Juanzi Li
Verlag: Springer International Publishing
Buch: Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data
Print ISBN: 978-3-030-01715-6

Electronic ISBN: 978-3-030-01716-3

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-030-01716-3_10

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