Adapting word2vec to Named Entity Recognition
Objekt
Titel
Adapting word2vec to Named Entity Recognition
Urheber
Scharolta Katharina Sienčnik
Zusammenfassung
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.
Datum
2015
Is Part Of
Adapting word2vec to Named Entity Recognition
presented at
Proceedings of the 20th Nordic Conference of Computational Linguistics, NODALIDA 2015, May 11-13, 2015, Vilnius, Lithuania
Verleger
Link�ping University Electronic Press, Link�pings universitet
Seiten
239-243
isbn
1650-3740