Big Data-Assisted Word Sense Disambiguation for Sign Language

Abstract

Automatic word sense disambiguation (WSD) from text is a task of great importance in various applications of natural language processing, for example, in machine translation, question answering, automatic summarization or sentiment analysis. There are different approaches to finding the meaning of a word within a context, whether using supervised, unsupervised, semi-supervised or knowledge-based methods. Several studies have been conducted to automatically translate from text to sign language, reproducing the result of the translation with a signing avatar, in a way that deaf users have access to informative contents that otherwise are highly inaccessible, because sign language is their mother tongue. The many proposals that have been made look forward to minimize these informative and communicative barriers. Sign languages, however, do not have as many words as the spoken languages, so an automatic translation must be as accurate and free of ambiguities as possible. In this paper, we propose to evaluate the use of public access big data resources, as well as appropriate techniques to access this type of resources for WSD tasks, illustrating their effects in a translation system from text in Spanish to Costa Rican Sign Language (LESCO). The architecture of the actual system incorporates the use of a folksonomy, from which the disambiguation process will benefit. When an exact word is not found for a given detected sense in the source text, the ontology will be fed back with a new relationship of hyperonymy, to alert the curator on the need to propose a new sign in that category, thus promoting an enrichment in a key component of the architecture. As a result of the evaluation, the most appropriate big data public resources and techniques for WSD for sign language will be elucidated.