Article | Proceedings of the 20th Nordic Conference of Computational Linguistics, NODALIDA 2015, May 11-13, 2015, Vilnius, Lithuania | Adapting word2vec to Named Entity Recognition
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Title:
Adapting word2vec to Named Entity Recognition
Author:
Scharolta Katharina Sienčnik: Department of Swedish / Department of Philosophy, Linguistics and Theory of Science, University of Gothenburg, Sweden
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Full text (pdf)
Year:
2015
Conference:
Proceedings of the 20th Nordic Conference of Computational Linguistics, NODALIDA 2015, May 11-13, 2015, Vilnius, Lithuania
Issue:
109
Article no.:
030
Pages:
239-243
No. of pages:
5
Publication type:
Abstract and Fulltext
Published:
2015-05-06
ISBN:
978-91-7519-098-3
Series:
Linköping Electronic Conference Proceedings
ISSN (print):
1650-3686
ISSN (online):
1650-3740
Series:
NEALT Proceedings Series
Publisher:
Linköping University Electronic Press, Linköpings universitet


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In this paper we explore how word vectors built using word2vec can be used to improve the performance of a classifier during Named Entity Recognition. Thereby, we discuss the best integration of word embeddings into the classification problem and consider the effect of the size of the unlabelled dataset on performance, reaching the unexpected result that for this particular task increasing the amount of unlabelled data does not necessarily increase the performance of the classifier.

Proceedings of the 20th Nordic Conference of Computational Linguistics, NODALIDA 2015, May 11-13, 2015, Vilnius, Lithuania

Author:
Scharolta Katharina Sienčnik
Title:
Adapting word2vec to Named Entity Recognition
References:

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Proceedings of the 20th Nordic Conference of Computational Linguistics, NODALIDA 2015, May 11-13, 2015, Vilnius, Lithuania

Author:
Scharolta Katharina Sienčnik
Title:
Adapting word2vec to Named Entity Recognition
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