TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Aga, Rosa Tsegaye
A1  - Drumond, Lucas
A1  - Wartena, Christian
A1  - Schmidt-Thieme, Lars
T1  - Integrating distributional and lexical information for semantic classification of words using MRMF
T2  - Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, Osaka, Japan, December 11-17 2016
N2  - 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 Multirelational 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.
KW  - Klassifikation
KW  - Semantik
Y1  - 2016
UN  - https://nbn-resolving.org/urn:nbn:de:bsz:960-opus4-11153
U6  - https://doi.org/10.25968/opus-1115
DO  - https://doi.org/10.25968/opus-1115
SP  - 2708
EP  - 2717
ER  -