Disambiguating Arabic Words According to Their Historical Appearance in the Document Based on Recurrent Neural Networks

Abstract

How can we determine the semantic meaning of a word in relation to its context of appearance? We eventually have to grabble with this difficult question, as one of the paramount problems of Natural Language Processing (NLP). In other words, this issue is commonly defined as Word Sense Disambiguation (WSD). The latter is one of the crucial difficulties within the NLP field. In this respect, word vectors extracted from a neural network model have been successfully applied for resolving the WSD problem. Accordingly, this article presents an unprecedented method to disambiguate Arabic words according to both their contextual appearance in a source text and the era in which they emerged. In fact, in the few previous decades, many researchers have been grabbling with Arabic Word Sense Disambiguation. It should be noted that the Arabic language can be divided into three major historical periods: old Arabic, middle-age Arabic, and contemporary Arabic. Actually, contemporary Arabic has proved to be the greatest concern of many researchers. The main gist of our work is to disambiguate Arabic words according to the historical period in which they appeared. To perform such a task, we suggest a method that deploys contextualized word embeddings to better gather valid syntactic and semantic information of the same word by taking into account its contextual uses. The preponderant thing is to convert both the senses and the contextual uses of an ambiguous item to vectors, then determine which of the possible conceptual meanings of the target word is closer to the given context.