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2017 | OriginalPaper | Buchkapitel

XLink: An Unsupervised Bilingual Entity Linking System

verfasst von : Jing Zhang, Yixin Cao, Lei Hou, Juanzi Li, Hai-Tao Zheng

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

Verlag: Springer International Publishing

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Abstract

Entity linking is a task of linking mentions in text to the corresponding entities in a knowledge base. Recently, entity linking has received considerable attention and several online entity linking systems have been published. In this paper, we build an online bilingual entity linking system XLink, which is based on Wikipeida and Baidu Baike. XLink conducts two steps to link the mentions in the input document to entities in knowledge base, namely mention parsing and entity disambiguation. To eliminate dependency of language, we conduct mention parsing without any named entity recognition tools. To ensure the correctness of linking results, we propose an unsupervised generative probabilistic method and utilize text and knowledge joint representations to perform entity disambiguation. Experiments show that our system gets a state-of-the-art performance and a high time efficiency.

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Vorheriges Kapitel Collective Entity Linking on Relational Graph Model with Mentions

Nächstes Kapitel Using Cost-Sensitive Ranking Loss to Improve Distant Supervised Relation Extraction

https://github.com/masha-p/PPRforNED.

https://github.com/NLPchina/ansj_seg.

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Titel: XLink: An Unsupervised Bilingual Entity Linking System
verfasst von: Jing Zhang
Yixin Cao
Lei Hou
Juanzi Li
Hai-Tao Zheng
Verlag: Springer International Publishing
Buch: Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data
Print ISBN: 978-3-319-69004-9

Electronic ISBN: 978-3-319-69005-6

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-69005-6_15

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