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Integrating Distributional and Lexical Information for Semantic Classification of Words using MRMF
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.
Learning Thesaurus Relations from Distributional Features
The similarity of words can be computed by comparing their feature vectors.
Multi-Relational Learning at Scale with ADMM
Learning from multiple-relational data which contains noise, ambiguities, or duplicate entities is essential to a wide range of applications such as statistical inference based on Web Linked Data, recommender systems, computational biology, and natural language processing.
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