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Towards Word Sense Disambiguation by Reasoning

11 pagesPublished: March 11, 2020


In this paper, we describe a practical application of Vampire for Word Sense Disam- biguation (WSD), which is an important research area in the field of Natural Language Processing (NLP). Its objective is choosing the intended sense of a word in a given context. In particular, we propose a method for the automatic disambiguation of the semantic rela- tions in BLESS, which is a dataset designed to evaluate models of distributional semantics, by choosing the WordNet synset it belongs to. For this purpose, we use the knowledge in Adimen-SUMO, which is obtained by means of a suitable transformation of the knowledge in the core of SUMO1 into first-order logic (FOL) and enables its use by FOL automated theorem provers such as Vampire. By exploiting the semantic mapping between WordNet and SUMO, we apply a black-box testing method that enables the automatic creation a set of conjectures for each word pair by considering the semantic relations provided by BLESS. Then, these conjectures are evaluated using Vampire and, according to the outcomes, each word is disambiguated to a single synset. Finally, we compare the results provided by our proposal and different disambiguation systems that can be found in the literature.

Keyphrases: automated theorem proving, Natural Language Processing, Ontologies, semantic mappings, SUMO

In: Laura Kovács and Andrei Voronkov (editors). Vampire 2018 and Vampire 2019. The 5th and 6th Vampire Workshops, vol 71, pages 19--29

BibTeX entry
  author    = {Javier \textbackslash{}'Alvez and Itziar Gonzalez-Dios and German Rigau},
  title     = {Towards Word Sense Disambiguation by Reasoning},
  booktitle = {Vampire 2018 and Vampire 2019. The 5th and 6th Vampire Workshops},
  editor    = {Laura Kovacs and Andrei Voronkov},
  series    = {EPiC Series in Computing},
  volume    = {71},
  pages     = {19--29},
  year      = {2020},
  publisher = {EasyChair},
  bibsource = {EasyChair,},
  issn      = {2398-7340},
  url       = {},
  doi       = {10.29007/s4th}}
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