Word Sense Disambiguation for Punjabi Language

Abstract

The recent eruption of data in different natural languages on the internet has necessitated the development of Natural Language Processing (NLP) tasks. The major impediments in the development and implementation of NLP are the scarcity of the standard datasets, knowledge resources, language tools and ambiguity resolution. Word Sense Disambiguation (WSD) is a critical and essential task for machine translation, information retrieval, question answering and sentiment analysis, etc. NLP tasks. The objective of WSD is to automatically select the appropriate sense of an ambiguous word based on the context of the word. The WSD process identifies the different senses for every word relevant to the text or discourse under consideration from the sense inventories such as dictionaries, thesaurus, ontologies, and WordNet. Then it involves a mean to assign the appropriate sense to each occurrence of a word in context. Thus WSD needs the representation of common sense and encyclopaedic knowledge to resolve the sense of ambiguous words. Recognizing the proper sense of a word in context by a computer is defined as an AI-complete complexity problem. There are two different types of WSD, namely targeted WSD and allwords WSD. The targeted WSD resolves the ambiguity of an ambiguous target word, usually occurring one per sentence. The all-words WSD disambiguates all open-class words (noun, adverb, verb, adjectives) in a text. In this research work, targeted WSD was implemented. India is a multilingual country having 22 national languages. Interlanguage processing tasks like machine translation, question answering, sentiment analysis, cross-lingual search, etc., are highly applicable problems in India.