Unsupervised ranking of knowledge bases for named entity recognition

Yassine Mrabet, Halil Kilicoglu, Dina Demner-Fushman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

With the continuous growth of freely accessible knowledge bases and the heterogeneity of textual corpora, selecting the most adequate knowledge base for named entity recognition is becoming a challenge in itself. In this paper, we propose an unsupervised method to rank knowledge bases according to their adequacy for the recognition of named entities in a given corpus. Building on a state-of-the-art, unsupervised entity linking approach, we propose several evaluation metrics to measure the lexical and structural adequacy of a knowledge base for a given corpus. We study the correlation between these metrics and three standard performance measures: precision, recall and F1 score. Our multi-domain experiments on 9 different corpora with 6 knowledge bases show that three of the proposed metrics are strong performance predictors having 0.62 to 0.76 Pearson correlation with precision and 0.96 correlation with both recall and F1 score.

Original languageEnglish (US)
Title of host publicationFrontiers in Artificial Intelligence and Applications
EditorsGal A. Kaminka, Frank Dignum, Eyke Hullermeier, Paolo Bouquet, Virginia Dignum, Maria Fox, Frank van Harmelen
PublisherIOS Press
Pages1248-1255
Number of pages8
ISBN (Electronic)9781614996712
DOIs
StatePublished - Jan 1 2016
Externally publishedYes
Event22nd European Conference on Artificial Intelligence, ECAI 2016 - The Hague, Netherlands
Duration: Aug 29 2016Sep 2 2016

Publication series

NameFrontiers in Artificial Intelligence and Applications
Volume285
ISSN (Print)0922-6389

Other

Other22nd European Conference on Artificial Intelligence, ECAI 2016
CountryNetherlands
CityThe Hague
Period8/29/169/2/16

ASJC Scopus subject areas

  • Artificial Intelligence

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