%0 Conference Proceedings
%T Comparison of the Best Parameter Settings in the Creation and Comparison of Feature Vectors in Distributional Semantic Models Across Multiple Languages
%+ University of Szeged [Szeged]
%A Dobó, András
%A Csirik, János
%Z Part 10: Machine Learning - Natural Language
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B 15th IFIP International Conference on Artificial Intelligence Applications and Innovations (AIAI)
%C Hersonissos, Greece
%Y John MacIntyre
%Y Ilias Maglogiannis
%Y Lazaros Iliadis
%Y Elias Pimenidis
%I Springer International Publishing
%3 Artificial Intelligence Applications and Innovations
%V AICT-559
%P 487-499
%8 2019-05-24
%D 2019
%R 10.1007/978-3-030-19823-7_41
%K Distributional semantic models
%K Semantic similarity and relatedness
%K Best combination of parameter settings
%K Comparison of findings across languages
%K English
%K Spanish and Hungarian
%Z Computer Science [cs]Conference papers
%X Measuring the semantic similarity and relatedness of words is important for many natural language processing tasks. Although distributional semantic models designed for this task have many different parameters, such as vector similarity measures, weighting schemes and dimensionality reduction techniques, there is no truly comprehensive study simultaneously evaluating these parameters while also analysing the differences in the findings for multiple languages. We would like to address this gap with our systematic study by searching for the best combination of parameter settings in the creation and comparison of feature vectors in distributional semantic models for English, Spanish and Hungarian separately, and then comparing our findings across these languages.During our extensive analysis we test a large number of possible settings for all parameters, with more than a thousand novel variants in case of some of them. As a result of this we were able to find such combinations of parameter settings that significantly outperform conventional settings combinations and achieve state-of-the-art results.
%G English
%Z TC 12
%Z WG 12.5
%2 https://inria.hal.science/hal-02331340/document
%2 https://inria.hal.science/hal-02331340/file/483292_1_En_41_Chapter.pdf
%L hal-02331340
%U https://inria.hal.science/hal-02331340
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-WG
%~ IFIP-TC12
%~ IFIP-AIAI
%~ IFIP-WG12-5
%~ IFIP-AICT-559