Year: 2015
Type: article
Abstract: 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.
Author: Scharolta Katharina Sienċnik
Institution: University of Gothenburg
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: Vetenskapsrådet
OpenAlex ID: W2251404238