Bridging Natural Language Processing and Psycholinguistics: computationally grounded semantic similarity datasets for Basque and Spanish
This work addresses a gap in psycholinguistic research for Basque and Spanish, but it is incremental as it extends existing methods to new languages and data.
The authors tackled the lack of psycholinguistic semantic similarity datasets by creating a computationally-grounded dataset for Basque and Spanish, which includes noun pairs with controlled variables and three types of similarity measurements derived from text, WordNet, and hybrid embeddings.
We present a computationally-grounded word similarity dataset based on two well-known Natural Language Processing resources; text corpora and knowledge bases. This dataset aims to fulfil a gap in psycholinguistic research by providing a variety of quantifications of semantic similarity in an extensive set of noun pairs controlled by variables that play a significant role in lexical processing. The dataset creation has consisted in three steps, 1) computing four key psycholinguistic features for each noun; concreteness, frequency, semantic and phonological neighbourhood density; 2) pairing nouns across these four variables; 3) for each noun pair, assigning three types of word similarity measurements, computed out of text, Wordnet and hybrid embeddings. The present dataset includes noun pairs' information in Basque and European Spanish, but further work intends to extend it to more languages.