Hits 1.181 – 1.194 of 1.194
| 1181 |
Using the Outlier Detection Task to Evaluate Distributional Semantic Models
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| 1182 |
Explorando métodos non-supervisados para calcular a similitude semántica textual ; Exploring Unsupervised Methods to Sematic Textual Similarity
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| 1183 |
Compositional Distributional Semantics with Syntactic Dependencies and Selectional Preferences
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Abstract:
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 ; 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) ; SI
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Keyword:
Compositional distributional semantics; Compositionality; Contextualized word embeddings; Dependency parsing; Meaning construction; Sentence BERT; Transformer architecture
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URL: https://doi.org/10.3390/app11125743
http://hdl.handle.net/10347/26678
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