We present a new hybrid lexical knowledge base that combines the contextual information of distributional models with the conciseness and precision of manually constructed lexical networks. The computation of our count-based distributional model includes the induction of word senses for single-word and multi-word terms, the disambiguation of word similarity lists, taxonomic relations extracted by patterns and context clues for disambiguation in context. In contrast to dense vector representations, our resource is human readable and interpretable, and thus can be easily embedded within the Semantic Web ecosystem.
CITATION STYLE
Faralli, S., Panchenko, A., Biemann, C., & Ponzetto, S. P. (2016). Linked disambiguated distributional semantic networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9982 LNCS, pp. 56–64). Springer Verlag. https://doi.org/10.1007/978-3-319-46547-0_7
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