Recently there is a surge in interest in learning vector representations of words using huge corpus in unsupervised manner. Such word vector representations, also known as word embedding, have been shown to improve the performance of machine learning models in several NLP tasks. However efficiency of such representation has not been systematically evaluated in biomedical domain. In this work our aim is to compare the performance of two state-of-the-art word embedding methods, namely word2vec and GloVe on a basic task of reflecting semantic similarity and relatedness of biomedical concepts. For this, vector representations of all unique words in the corpus of more than 1 million full-length research articles in biomedical domain are obtained from the two methods. We observe that parameters of these models do affect their ability to capture lexico-semantic properties and word2vec with particular language modeling seems to perform better than others.
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