Explorando métodos non-supervisados para calcular a similitude semántica textual
ISSN: 1647-0818
Ano de publicación: 2018
Volume: 10
Número: 2
Páxinas: 63-68
Tipo: Artigo
Outras publicacións en: Linguamática
Obxectivos de Desenvolvemento Sustentable
Resumo
This paper presents some unsupervised methods for detecting semantic textual similarity, which are based on distributional models and dependency parsing. The systems are evaluated using the dataset realased by the ASSIN Shared Task co-located with PROPOR 2016. The more basic methods offer better behavior than the more complex ones, which include syntactic-semantic information in sentence analysis. Finally, the use of distributional models built automatically from corpora provides results comparable to strategies that use external lexical resources built manually.