RT Journal Article
T1 Compositional Distributional Semantics with Syntactic Dependencies and Selectional Preferences
A1 Gamallo Otero, Pablo
K1 Compositionality
K1 Dependency parsing
K1 Meaning construction
K1 Compositional distributional semantics
K1 Transformer architecture
K1 Contextualized word embeddings
K1 Sentence BERT
AB This article describes a compositional model based on syntactic dependencies which has been designed to build contextualized word vectors, by following linguistic principles related to the concept of selectional preferences. The compositional strategy proposed in the current work has been evaluated on a syntactically controlled and multilingual dataset, and compared with Transformer BERT-like models, such as Sentence BERT, the state-of-the-art in sentence similarity. For this purpose, we created two new test datasets for Portuguese and Spanish on the basis of that defined for the English language, containing expressions with noun-verb-noun transitive constructions. The results we have obtained show that the linguistic-based compositional approach turns out to be competitive with Transformer models
PB MDPI
YR 2021
FD 2021
LK http://hdl.handle.net/10347/26678
UL http://hdl.handle.net/10347/26678
LA eng
NO Appl. Sci. 2021, 11(12), 5743; https://doi.org/10.3390/app11125743
NO This work has received financial support from DOMINO project (PGC2018-102041-B-I00, MCIU/AEI/FEDER, UE), eRisk project (RTI2018-093336-B-C21), the Consellería de Cultura, Educación e Ordenación Universitaria (accreditation 2016-2019, ED431G/08, Groups of Reference: ED431C 2020/21, and ERDF 2014-2020: Call ED431G 2019/04) and the European Regional Development Fund (ERDF)
DS Minerva
RD 24 abr 2026