A Lightweight Regression Method to Infer Psycholinguistic Properties for Brazilian Portuguese
This provides a practical solution for less-resourced languages like Brazilian Portuguese in NLP tasks such as text simplification, though it is incremental as it adapts existing methods to a new language context.
The study tackled the problem of automatically inferring psycholinguistic properties for Brazilian Portuguese, which lacks resources for such tasks, by using a lightweight regression method with basic features like word length and frequency lists. The result was a resource of 26,874 words annotated with properties like concreteness, achieving correlations close to existing works.
Psycholinguistic properties of words have been used in various approaches to Natural Language Processing tasks, such as text simplification and readability assessment. Most of these properties are subjective, involving costly and time-consuming surveys to be gathered. Recent approaches use the limited datasets of psycholinguistic properties to extend them automatically to large lexicons. However, some of the resources used by such approaches are not available to most languages. This study presents a method to infer psycholinguistic properties for Brazilian Portuguese (BP) using regressors built with a light set of features usually available for less resourced languages: word length, frequency lists, lexical databases composed of school dictionaries and word embedding models. The correlations between the properties inferred are close to those obtained by related works. The resulting resource contains 26,874 words in BP annotated with concreteness, age of acquisition, imageability and subjective frequency.