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TwitterNEED: A hybrid approach for named entity extraction and disambiguation for tweet*

Published online by Cambridge University Press:  10 July 2015

MENA B. HABIB  and
MAURICE VAN KEULEN
MENA B. HABIB
Affiliation:
Database Chair, University of Twente, Enschede, the Netherlands e-mail: m.b.habib@ewi.utwente.nl, m.vankeulen@ewi.utwente.nl
MAURICE VAN KEULEN
Affiliation:
Database Chair, University of Twente, Enschede, the Netherlands e-mail: m.b.habib@ewi.utwente.nl, m.vankeulen@ewi.utwente.nl
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Abstract

Twitter is a rich source of continuously and instantly updated information. Shortness and informality of tweets are challenges for Natural Language Processing tasks. In this paper, we present TwitterNEED, a hybrid approach for Named Entity Extraction and Named Entity Disambiguation for tweets. We believe that disambiguation can help to improve the extraction process. This mimics the way humans understand language and reduces error propagation in the whole system. Our extraction approach aims for high extraction recall first, after which a Support Vector Machine attempts to filter out false positives among the extracted candidates using features derived from the disambiguation phase in addition to other word shape and Knowledge Base features. For Named Entity Disambiguation, we obtain a list of entity candidates from the YAGO Knowledge Base in addition to top-ranked pages from the Google search engine for each extracted mention. We use a Support Vector Machine to rank the candidate pages according to a set of URL and context similarity features. For evaluation, five data sets are used to evaluate the extraction approach, and three of them to evaluate both the disambiguation approach and the combined extraction and disambiguation approach. Experiments show better results compared to our competitors DBpedia Spotlight, Stanford Named Entity Recognition, and the AIDA disambiguation system.

Type
Articles
Information
Natural Language Engineering , Volume 22 , Issue 3 , May 2016 , pp. 423 - 456
Copyright
Copyright © Cambridge University Press 2015 

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Footnotes

*

The authors would like to thank Zhemin Zhu for sharing his CRF model (Zhu et al.2013) and assisting us in applying it. This work is supported by the Dutch national research program COMMIT.

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