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        <title>Integrating Distributional and Lexical Information for Semantic Classification of Words using MRMF</title>
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            <namePart type="given">Yuji</namePart>
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    <abstract>Semantic classification of words using distributional features is usually based on the semantic similarity of words. We show on two different datasets that a trained classifier using the distributional features directly gives better results. We use Support Vector Machines (SVM) and Multi-relational Matrix Factorization (MRMF) to train classifiers. Both give similar results. However, MRMF, that was not used for semantic classification with distributional features before, can easily be extended with more matrices containing more information from different sources on the same problem. We demonstrate the effectiveness of the novel approach by including information from WordNet. Thus we show, that MRMF provides an interesting approach for building semantic classifiers that (1) gives better results than unsupervised approaches based on vector similarity, (2) gives similar results as other supervised methods and (3) can naturally be extended with other sources of information in order to improve the results.</abstract>
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%0 Conference Proceedings
%T Integrating Distributional and Lexical Information for Semantic Classification of Words using MRMF
%A Aga, Rosa Tsegaye
%A Drumond, Lucas
%A Wartena, Christian
%A Schmidt-Thieme, Lars
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F aga-etal-2016-integrating
%X Semantic classification of words using distributional features is usually based on the semantic similarity of words. We show on two different datasets that a trained classifier using the distributional features directly gives better results. We use Support Vector Machines (SVM) and Multi-relational Matrix Factorization (MRMF) to train classifiers. Both give similar results. However, MRMF, that was not used for semantic classification with distributional features before, can easily be extended with more matrices containing more information from different sources on the same problem. We demonstrate the effectiveness of the novel approach by including information from WordNet. Thus we show, that MRMF provides an interesting approach for building semantic classifiers that (1) gives better results than unsupervised approaches based on vector similarity, (2) gives similar results as other supervised methods and (3) can naturally be extended with other sources of information in order to improve the results.
%U https://aclanthology.org/C16-1255
%P 2708-2717

Markdown (Informal)

[Integrating Distributional and Lexical Information for Semantic Classification of Words using MRMF](https://aclanthology.org/C16-1255) (Aga et al., COLING 2016)

• Integrating Distributional and Lexical Information for Semantic Classification of Words using MRMF (Aga et al., COLING 2016)

ACL

• Rosa Tsegaye Aga, Lucas Drumond, Christian Wartena, and Lars Schmidt-Thieme. 2016. Integrating Distributional and Lexical Information for Semantic Classification of Words using MRMF. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2708–2717, Osaka, Japan. The COLING 2016 Organizing Committee.