Compositional Semantics using Feature-Based Models from WordNet

Pablo Gamallo, Martín Pereira-Fariña


Abstract
This article describes a method to build semantic representations of composite expressions in a compositional way by using WordNet relations to represent the meaning of words. The meaning of a target word is modelled as a vector in which its semantically related words are assigned weights according to both the type of the relationship and the distance to the target word. Word vectors are compositionally combined by syntactic dependencies. Each syntactic dependency triggers two complementary compositional functions: the named head function and dependent function. The experiments show that the proposed compositional method outperforms the state-of-the-art for both intransitive subject-verb and transitive subject-verb-object constructions.
Anthology ID:
W17-1901
Volume:
Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications
Month:
April
Year:
2017
Address:
Valencia, Spain
Editors:
Jose Camacho-Collados, Mohammad Taher Pilehvar
Venue:
SENSE
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–11
Language:
URL:
https://aclanthology.org/W17-1901
DOI:
10.18653/v1/W17-1901
Bibkey:
Cite (ACL):
Pablo Gamallo and Martín Pereira-Fariña. 2017. Compositional Semantics using Feature-Based Models from WordNet. In Proceedings of the 1st Workshop on Sense, Concept and Entity Representations and their Applications, pages 1–11, Valencia, Spain. Association for Computational Linguistics.
Cite (Informal):
Compositional Semantics using Feature-Based Models from WordNet (Gamallo & Pereira-Fariña, SENSE 2017)
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PDF:
https://aclanthology.org/W17-1901.pdf