@InProceedings{aga-EtAl:2016:COLING,
  author    = {Aga, Rosa Tsegaye  and  Drumond, Lucas  and  Wartena, Christian  and  Schmidt-Thieme, Lars},
  title     = {Integrating Distributional and Lexical Information for Semantic Classification of Words using MRMF},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {2708--2717},
  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.},
  url       = {http://aclweb.org/anthology/C16-1255}
}