Comparing explicit and predictive distributional semantic models endowed with syntactic contexts

TítuloComparing explicit and predictive distributional semantic models endowed with syntactic contexts
AutoresPablo Gamallo
TipoArtículo de revista
Fonte Language Resources and Evaluation, SPRINGER, Vol. 3, No. 51, pp. 1-17 , 2017.
ISSN1574-020X
DOI10.1007/s10579-016-9357-4
AbstractIn this article, we introduce an explicit count-based strategy to build word space models with syntactic contexts (dependencies). A filtering method is defined to reduce explicit word-context vectors. This traditional strategy is compared with a neural embedding (predictive) model also based on syntactic dependencies. The comparison was performed using the same parsed corpus for both models. Besides, the dependency-based methods are also compared with bag-of-words strategies, both count-based and predictive ones. The results show that our traditional count-based model with syntactic dependencies outperforms other strategies, including dependency-based embeddings, but just for the tasks focused on discovering similarity between words with the same function (i.e. near-synonyms).
Palabras chaveWord similarity, Word embeddings, Count-based models, Dependency-based semantic models,