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Linked Disambiguated Distributional Semantic Networks

Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9982)

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.

Keywords

Lexical Knowledge Base Word Similarity Lists BabelNet Sense Inventory Distributional Thesaurus (DT) 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Notes

Acknowledgments

We acknowledge the support of the Deutsche Forschungsgemeinschaft (DFG) under the JOIN-T project.

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© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Data and Web Science GroupUniversity of MannheimMannheimGermany
  2. 2.Language Technology GroupTU DarmstadtDarmstadtGermany

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