While neural embeddings represent a popular choice for word representation in a wide variety of NLP tasks, their usage for thematic fit modeling has been limited, as they have been reported to lag behind syntax-based count models. In this paper, we propose a complete evaluation of count models and word embeddings on thematic fit estimation, by taking into account a larger number of parameters and verb roles and introducing also dependency-based embeddings in the comparison. Our results show a complex scenario, where a determinant factor for the performance seems to be the availability to the model of reliable syntactic information for building the distributional representations of the roles.

Are word embeddings really a bad fit for the estimation of thematic fit? / Chersoni, E.; Pannitto, L.; Santus, E.; Lenci, A.; Huang, C. -R.. - (2020), pp. 5708-5713. (Intervento presentato al convegno 12th International Conference on Language Resources and Evaluation, LREC 2020 tenutosi a Palais du Pharo, fra nel 2020).

Are word embeddings really a bad fit for the estimation of thematic fit?

Pannitto L.;
2020-01-01

Abstract

While neural embeddings represent a popular choice for word representation in a wide variety of NLP tasks, their usage for thematic fit modeling has been limited, as they have been reported to lag behind syntax-based count models. In this paper, we propose a complete evaluation of count models and word embeddings on thematic fit estimation, by taking into account a larger number of parameters and verb roles and introducing also dependency-based embeddings in the comparison. Our results show a complex scenario, where a determinant factor for the performance seems to be the availability to the model of reliable syntactic information for building the distributional representations of the roles.
2020
LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings
Marseille, France
European Language Resources Association (ELRA)
Chersoni, E.; Pannitto, L.; Santus, E.; Lenci, A.; Huang, C. -R.
Are word embeddings really a bad fit for the estimation of thematic fit? / Chersoni, E.; Pannitto, L.; Santus, E.; Lenci, A.; Huang, C. -R.. - (2020), pp. 5708-5713. (Intervento presentato al convegno 12th International Conference on Language Resources and Evaluation, LREC 2020 tenutosi a Palais du Pharo, fra nel 2020).
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/286117
 Attenzione

Attenzione! I dati visualizzati non sono stati sottoposti a validazione da parte dell'ateneo

Citazioni
  • ???jsp.display-item.citation.pmc??? ND
  • Scopus 4
  • ???jsp.display-item.citation.isi??? ND
  • OpenAlex ND
social impact