#WHO USES BABELNET FOR FREE#

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#WHO USES BABELNET FOR FREE#

Ng, It makes sense: A wide-coverage word sense disambiguation system for free text, Proc. Teh, NUS-ML: Improving word sense disambiguation using topic features, Proc. van den Bosch, Gambl, genetic algorithm optimization of memory-based WSD, Proc. Navigli, A quick tour of word sense disambiguation, induction and related approaches, Proc. Navigli, Word sense disambiguation: A survey, ACM Comput. In this paper, we focus on the case study of BabelNet, a large multilingual encyclopedic dictionary and semantic network, to describe in detail how such knowledge resources are built, improved and exploited for crucial NLP tasks such as Word Sense Disambiguation, Entity Linking and Semantic Similarity. In the last two decades, research in lexical semantics (which focuses on the meaning of individual linguistic elements, i.e., words and expressions), has produced increasingly comprehensive and effective machine-readable dictionaries in multiple languages: like humans, NLP systems can now leverage these sources of lexical knowledge to discriminate among various senses of a given lexeme, thereby improving their performances on downstream tasks and applications. A complete semantic model of language, however, needs first of all reliable building blocks. Getting a handle on the various phenomena that regulate the meaning of linguistic utterances can pave the way for solving many compelling and ambitious tasks in the field, from Machine Translation to Question Answering and Information Retrieval. Accurate semantic modeling lies at the very core of today’s Natural Language Processing (NLP).

Who uses babelnet