Little Wins: Collecting, Preparing and Publishing Resources for Assamese Word Sense Disambiguation

Abstract

This paper presents the creation of the Assamese Ambiguous Sense Inventory (ASI) and Sense Annotated Data Set (SeAnDa) for the Assamese Word Sense Disambiguation (WSD) task. WSD is a computational process that identifies the appropriate sense of an ambiguous term relevant to the context. In this paper, we describe the process of creating ASI and SeAnDa for the implementation of the Assamese Supervised WSD task. The ASI consists of a database of ambiguous terms with their multiple senses, and based on the ASI, a sense-annotated dataset was prepared from the Assamese raw Corpus. The ambiguous terms are extracted from the Assamese WordNet and Corpus. Currently, we have an inventory of 100 ambiguous terms with their various glosses in both Assamese and English, and a sense annotated dataset of minimal size 2K sentences. The authors have analyzed the ambiguous words considering the parameters- Parts of speech and the number of senses. It is reported that most of the ambiguous terms in the inventory are nouns, and most of the terms have binary senses. The ASI and SeAnDa acts as the preliminary resources for implementing the Assamese Supervised WSD task with Iterative learning and Hold-out evaluation strategy. We here adopted and applied the Naïve Bayes Classifier achieving an accuracy of 71\%. As Assamese is a computationally low-resourced language, these resources will assist researchers and developers in their future research purpose.