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Vector Space Models in Detection of Semantically Non-compositional Word Combinations in Turkish

  • Levent Tolga Eren
  • Senem Kumova MetinEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11179)

Abstract

The semantic compositionality presents the relation between the meanings of word combinations and their components. Simply, in non-compositional expressions, the words combine to generate a different meaning. This is why, identification of non-compositional expressions (e.g. idioms) become important in natural language processing tasks such as machine translation and word sense disambiguation.

In this study, we explored the performance of vector space models in detection of non-compositional expressions in Turkish. A data set of 2229 uninterrupted two-word combinations that is built from six different Turkish corpora is utilized. Three sets of five different vector space models are employed in the experiments. The evaluation of models is performed using well-known accuracy and F-measures. The experimental results showed that the model that measures the similarity between the vectors of word combination and the second composing word produced higher average F-scores for all testing corpora.

Keywords

Semantic compositionality Vector space model Turkish 

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Faculty of Engineeringİzmir University of EconomicsİzmirTurkey

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