Genetic Algoritm Based Hindi Word Sense Disambigaution

Resolution of lexical ambiguity, commonly known as Word Sense Disambiguation (WSD) task is to distinguish the correct sense among the set of senses for an ambiguous term depending on the particular context automatically. It plays the vital role as it acts as an intermediate phase to many Natural Language Processing (NLP) applications like Machine Translation, Information Retrieval, Speech Processing, Hypertext navigation, Parts-of-Speech tagging. Existing literature reveals that there are various approaches for lexical ambiguity resolution-Knowledge based, Corpus based. In recent years, many WSD systems is being developed in Indian languages like Hindi, Malayalam, Manipuri, Nepali, Kannada but no such automated system has yet emerged for the Indo-Aryan language-Assamese. Our future work aims to develop a model for the WSD problem which is fast, optimal and efficient in terms of accuracy and scalability. This paper presents a survey report made in this research topic discussing the WSD problem, various approaches along with their algorithms. Moreover it also list out the various NLP applications which would be efficient when disambiguation system is merged. Evaluation measures used to determine the WSD performance are also discussed here.

Procedia Computer Science, 2015

Semantic relations play an important role in resolving the ambiguity of a polysemous word. This paper investigates the role of hypernym, hyponym, holonym and meronym relations in Hindi Word Sense Disambiguation. In this work, we have considered five different cases: all relations, hypernym and hyponym, hypernym, hyponym and holonym. No semantic relations are used in the baseline. We obtained an overall improvement of 12.09% in precision over the baseline for the case when all relations are considered. The maximum improvement for single semantic relation was obtained using hyponym which resulted in an overall improvement of 9.86% in precision.

2016

In natural languages, there are many words that have different meaning in different context. Word sense disambiguation is a method for identification of correct word sense in a specific context. Indian languages are morphologically rich in nature and hence show more inflections as compare to foreign languages. Therefore it is difficult to develop word sense disambiguation system for Indian languages. In this paper various efforts done by various researchers to develop word sense disambiguation systems for Indian languages have been discussed.

2013

These days, the language is making hindrances in the advantages of Information Technology revolution in India. So, there is the need of the adequate measures to perform natural language processing (NLP) through computer processing so that computer based system can be interacted by users through natural language like Hindi. This paper presents a new Word Sense Disambiguation method associated with mining association rules. This method can mine the association rules between the sense of the ambiguous word and its context using its various senses present in the Hindi WordNet. The sense of the ambiguous word assigned by choosing the sense which the most association rules refer. In this paper, we are concerned with association rule mining for word sense disambiguation for Hindi language and finding the correct sense for given Hindi word. We explore the multiple meanings of Hindi word with the help of Hindi Word net prepared by IIT Bombay. The experiment result shows that the method has h...

Regular, 2020

Word Sense Disambiguation (WSD) is a significant issue in Natural Language Processing (NLP). WSD refers to the capacity of recognizing the correct sense of a word in a given context. It can improve numerous NLP applications such as machine translation, text summarization, information retrieval, or sentiment analysis. This paper proposes an approach named ShotgunWSD. Shotgun WSD is an unsupervised and knowledgebased algorithm for global word sense disambiguation. The algorithm is motivated by the Shotgun sequencing technique. Shotgun WSD is proposed to disambiguate the word senses of Telugu document with three functional phases. The Shotgun WSD achieves the better performance than other approaches of WSD in the disambiguating sense of ambiguous words in Telugu documents. The dataset is used in the Indo-WordNet.

2016

Sense Disambiguation" of a word is a simple way of selecting proper sense (meaning) for an ambiguous word in a given context. Sense disambiguation of a word is very crucial, and its importance is used in every application of computational linguistics. There are many methods available in Natural Language Processing for performing the sense disambiguation. In our work, we are proposing the Graph-based algorithm through which the ambiguity is resolved for the words based on their senses and the context domain. Graph-based algorithm creates a graph comprising the word to be disambiguated along with their corresponding candidate sense. In the proposed work, sense disambiguation of word has been done for Marathi language words. Multiple meanings of Marathi word has been explored with the help of Marathi WordNet [4] prepared by IIT-Bombay.

Ambiguity is the big challenge in computational linguistics. Word sense disambiguation (WSD) is a natural classification problem. It is task to disambiguate the meaning of the word, which word has multiple senses. In natural language processing WSD is the crucial and challenging task in many applications, Such as machine translation, question answering system, multi-document summarization, information extraction and language generation.Marathi Language is predominantly spoken by the Marathi people of Maharashtra. It is an Indo-Aryan Language and officially uses in Maharashtra and Goa State. It is one of the twenty two scheduled languages of India. Lots of work is carried out for foreign languages using various technique of WSD as compare to Indian Languages. This paper is majorly focused on Marathi language word sense disambiguation. We discuss different techniques of WSD to disambiguate the Marathi language words.

ArXiv, 2019

A word having multiple senses in a text introduces the lexical semantic task to find out which particular sense is appropriate for the given context. One such task is Word sense disambiguation which refers to the identification of the most appropriate meaning of the polysemous word in a given context using computational algorithms. The language processing research in Hindi, the official language of India, and other Indian languages is restricted by unavailability of the standard corpus. For Hindi word sense disambiguation also, the large corpus is not available. In this work, we prepared the text containing new senses of certain words leading to the enrichment of the sense-tagged Hindi corpus of sixty polysemous words. Furthermore, we analyzed two novel lexical associations for Hindi word sense disambiguation based on the contextual features of the polysemous word. The evaluation of these methods is carried out over learning algorithms and favorable results are achieved.

Regular, 2020

Telugu (తెలుగు) is one of the Dravidian languages which are morphologically rich. As within the other languages, it too consists of ambiguous words/phrases which have one-of-a-kind meanings in special contexts. Such words are referred as polysemous words i.e. words having a couple of experiences. A Knowledge based approach is proposed for disambiguating Telugu polysemous phrases using the computational linguistics tool, IndoWordNet. The task of WSD (Word sense disambiguation) requires finding out the similarity among the target phrase and the nearby phrase. In this approach, the similarity is calculated either by means of locating out the range of similar phrases (intersection) between the glosses (definition) of the target and nearby words or by way of finding out the exact occurrence of the nearby phrase's sense in the hierarchy (hypernyms/hyponyms) of the target phrase's senses. The above parameters are changed by using the intersection use of not simplest the glosses but...