2016 NasariIntegratingExplicitKnowle

(Camacho-Collados et al., 2016) ⇒ Jose Camacho-Collados, Mohammad Taher Pilehvar, and Roberto Navigli. (2016). “Nasari: Integrating Explicit Knowledge And Corpus Statistics For A Multilingual Representation Of Concepts And Entities.” In: Elsevier - Artificial Intelligence, 240.

Subject Headings: Nasari System; Semantic Similarity System; SemEval-2017 Task 2; Word Embedding System; Semantic Word Similarity Benchmark Task.

Notes

Online Resource(s):
- Website: http://lcl.uniroma1.it/nasari/

Cited By

Google Scholar: ~ 174 Citations.

Quotes

Author Keywords

Semantic representation; Lexical semantics; Word Sense Disambiguation; Semantic similarity; Sense clustering; Domain labeling

Abstract

Owing to the need for a deep understanding of linguistic items, semantic representation is considered to be one of the fundamental components of several applications in Natural Language Processing and Artificial Intelligence. As a result, semantic representation has been one of the prominent research areas in lexical semantics over the past decades. However, due mainly to the lack of large sense-annotated corpora, most existing representation techniques are limited to the lexical level and thus cannot be effectively applied to individual word senses. In this paper we put forward a novel multilingual vector representation, called Nasari, which not only enables accurate representation of word senses in different languages, but it also provides two main advantages over existing approaches: (1) high coverage, including both concepts and named entities, (2) comparability across languages and linguistic levels (i.e., words, senses and concepts), thanks to the representation of linguistic items in a single unified semantic space and in a joint embedded space, respectively. Moreover, our representations are flexible, can be applied to multiple applications and are freely available at http://lcl.uniroma1.it/nasari/. As evaluation benchmark, we opted for four different tasks, namely, word similarity, sense clustering, domain labeling, and Word Sense Disambiguation, for each of which we report state-of-the-art performance on several standard datasets across different languages.

References

BibTeX

@article{2016_NasariIntegratingExplicitKnowle,
  author    = {Jose Camacho-Collados and
               Mohammad Taher Pilehvar and
               Roberto Navigli},
  title     = {Nasari: Integrating Explicit Knowledge And Corpus Statistics For A
               Multilingual Representation Of Concepts And Entities},
  journal   = {Artificial Intelligence},
  volume    = {240},
  pages     = {36--64},
  year      = {2016},
  publisher = {Elsevier}
  url       = {https://doi.org/10.1016/j.artint.2016.07.005},
  doi       = {10.1016/j.artint.2016.07.005},
}

	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2016 NasariIntegratingExplicitKnowle	Mohammad Taher Pilehvar Jose Camacho-Collados Roberto Navigli			Nasari: Integrating Explicit Knowledge And Corpus Statistics For A Multilingual Representation Of Concepts And Entities						2016