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.
Linked disambiguated distributional semantic networks / Faralli, S.; Panchenko, A.; Biemann, C.; Ponzetto, S. P.. - 9982:(2016), pp. 56-64. (Intervento presentato al convegno The Semantic Web – ISWC 2016 tenutosi a Kobe; Japan) [10.1007/978-3-319-46547-0_7].
Linked disambiguated distributional semantic networks
Faralli S.
Co-primo
;Ponzetto S. P.
Co-primo
2016
Abstract
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
