Semantic similarity between Turkish and European languages using word embeddings
Şenel, Lütfü Kerem
Proceedings of the IEEE 25th Signal Processing and Communications Applications Conference, SIU 2017
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Representation of words coming from vocabulary of a language as real vectors in a high dimensional space is called as word embeddings. Word embeddings are proven to be successful in modelling semantic relations between words and numerous natural language processing applications. Although developed mainly for English, word embeddings perform well for many other languages. In this study, semantic similarity between Turkish (two different corpora) and five basic European languages (English, German, French, Spanish, Italian) is calculated using word embeddings over a fixed vocabulary, obtained results are verified using statistical testing. Also, the effect of using different corpora, and additional preprocess steps on the performance of word embeddings on similarity and analogy test sets prepared for Turkish is studied.
KeywordsNatural language processing
Semantic similarity between languages
High dimensional spaces
Published Version (Please cite this version)http://dx.doi.org/10.1109/SIU.2017.7960365
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