A ssamese Word Sense Disambiguation using Genetic Algorithm (2020)

Abstract: Word sense disambiguation (WSD) is a problem to determine a word according to a context in which it occurs. There are plenty amount of works done in WSD for some languages such as English, but research work on Assamese WSD remains limited. It is a more exigent task because Assamese has an intrinsic complexity in its writing structure and ambiguity, such as syntactic, semantic, and anaphoric ambiguity levels.A novel unsupervised genetic word sense disambiguation algorithm is proposed in this paper. The algorithm first uses WordNet to extract all possible senses for a given ambiguous word, then a genetic algorithm is used taking Wu-Palmer’s similarity measure as the fitness function and calculating the similarity measure for all extracted senses. The winner sense which will have the highest score declared as he winner sense.

## introduction the specific task that resolves the lexical ambiguity is commonly referred to as word sense disambiguation(wsd). it is a process to assign a meaning to a word based on the context in which it occurs. wsd is quite important for resolving some problems in such nlp applications as text clustering and classification, word similarity computation, and so on. suppose there is a word জকা/jika. the word জকা/jika is an ambiguous word which has multiple senses. sense 1: জকা/@xmath0: লতাজতীয় গছত লগা তৰকাৰী িহচােপ খাৱা এিবধ দীঘল আ পাতল ফল /lota jatio gosot loga torkari hisape khuwa abhid dighol aru patol @xmath1. মােয় আ জ জকাৰ তৰকাৰী ৰাি েছ/maa'e aaji jikar torkari randhise/mother is cooking jika curry today. → this জকা/jika gives a sense as a kind of vegetable b.ৰাহ েল বাজী জিকল/rahul e baji jikil/rahul won the @xmath2. various wsd methods uses external knowledge source, for example, word net/ thesaurus as a basement to disambiguate the ambiguous words. there are also some hybrid methods, such as an iterative approach to wsd, sense learner which first used textual definitions of senses from a machine-readable dictionary and then used a corpus to identify and calculate the relatedness between two senses. another kind of approach to disambiguate words using machine learning-based algorithms @xcite rather than taking it directly from an explicit knowledge source such as: ## related works as no work on the unsupervised approach is done to date in the assamese language, we had considered work in indian languages done to date as assamese is one of the indian languages. most of the indian language structure is the subject-object-verb(sov). the proposed works in the unsupervised approach are as follows: a work on malayalam language wsd have proposed by @xcite . in this paper, an unsupervised approach has been proposed and the proposed system makes use of a corpus which is taken from various malayalam web documents. based on the similarity between the given input and the sense clusters, the most similar sense is selected as the winner sense. the proposed system gives an accuracy of 72 percent. a work on gujarati language wsd have proposed by @xcite . in this paper, a genetic algorithm has been proposed which uses a knowledge-based approach where indo-aryan wordnet for gujarati is used as a lexical database for wsd . a work on hindi language wsd have proposed by @xcite . in this paper, an unsupervised approach has been proposed and applied the fuzzy c-means clustering algorithm to form clusters. they test the approach on the corpus created using hindi news articles and wikipedia and then compare the approach with other methods available in all the previous works done for the hindi language. the training data used consists of 3753 words in total and found an accuracy of approximately79.16 percentage . a work on hindi language wsd have proposed by @xcite . in this paper, a genetic algorithm has been proposed and this is the very first use of a genetic algorithm for the hindi language wsd. here, hindi wordnet has been used as a lexical database for wsd. to date, they have found some results which are under process. a work on gujarati language wsd have proposed by @xcite have proposed "word sense disambiguation for the marathi language". in this paper, they are proposing the genetic algorithm approach for the disambiguation of ambiguous words. in their survey, it is found that a genetic algorithm gives better results than other approaches a work on tamil language wsd have proposed by @xcite . in this paper, the hierarchical fuzzy clustering algorithm is applied along with the wordnet dictionary. to identify the disambiguated words, sense identification is performed for the adjectives, and comparison is performed. on comparing with previous methods for wsd in the tamil language, their work gives a better result with a percentage of 91.2 percentage . a work on bengali language wsd have proposed by @xcite . in this paper, word sense disambiguation has been done using the unsupervised methodology. in this work, clustering has been implemented using the weka tool and the test sentences are extracted from the bengali text corpus developed in the tdil (technology development for indian language) project of the govt. of india. a work on assamese language wsd have proposed by @xcite using a supervised machine learning approach "decision tree-based". in this approach, a few polysemous words with different real occurrences in assamese text with manual sense annotation were collected as the training and test dataset. they have been able to get an average f-measure of .611 when 10-fold cross-validation evaluation when performed on 10 assamese ambiguous words. a work on assamese language wsd have proposed by @xcite . this is the first work to the best of our knowledge on developing an automatic wsd system for the assamese language using naive bayes approach. they have conducted the experiment using 25 highly polysemic words using the articles collected from the assamese textbook of class x. a work on assamese language wsd have proposed by @xcite in which the walker algorithm, a thesaurus based algorithm, is applied that makes use of the category of each word as defined in the thesaurus. but for a language like assamese, wsd is far from being a topical approach. moreover, due to the non-existing thesaurus with a tagged category particularly for the assamese language. ## methodology the initial idea of genetic word sense disambiguation(gwsd) comes from disambiguating a set of domain words extracted from a domain corpus. because the terms can belong to the same domain and we can assume that they tend to be similar in semantics with each other. so a good disambiguation solution should be a solution that assigns one sense to each term and increases the overall similarity on the set of selected senses. each sense of a word corresponds to a synset present in the wordnet and hence we use genetic algorithms to find an optimal set of senses, which increases the overall similarity as it is a global search heuristic. for the wsd task, in a given population each chromosome which is equal to the all possible sense for a given term in the wordnet will be tested using a fitness function, and the most fitted chromosome or sense will be declared as the correct sense. we give a simple example of initialization. if we want to disambiguate a word, the population size is equaled to the collection of all the possible senses from the wordnet, and for each sense, we calculate the fitness function, and the fittest sense is declared as the winner sense i.e which sense scores the maximum similarity using the fitness function. a fitness function is an objective function that is used to summarize, as a single figure of merit, how close a given design solution is to achieving the set aims. fitness functions are used in genetic programming and genetic algorithms to guide simulations towards optimal design solutions. in our proposed approach, we are using conceptual similarity between two senses using wu-palmer's similarity measure as the fitness function as described in section 3.1. for example: ভাল/bhul/vegetable. there a total of 2 sense for ভাল/bhul/vegetable so 2 will be the population size and then we calculate the fitness function as mentioned above. in figure 1 , after we take the input at first initialize the initial population i.e collect all the possible senses from the wordnet. for each chromosome/sense in the population, test the fitness function(wu-palmer's similarity measure). the sense/ chromosome which gets the maximum similarity to assign declared as the winner sense i.e required output. ## data set and experiment we evaluate the genetic algorithm developed and applied a sense annotated assamese corpus as shown in the table below. the sense inventory used in this research has been derived from the assamese wordnet. there are a total of 50000 words and among them, 15606 words are ambiguous @xcite . as no unsupervised works are done earlier, our work is compared with the available works related to supervised approaches in the assamese language.though we have been able to get a few papers only which have calculated their results in precision and recall metrics, we have been able to get quite better results while comparing. ## conclusion in this paper, the wsd system for the assamese language using a genetic algorithm has been proposed and analyzed. works on assamese language using genetic algorithm are almost none. therefore we have an attempt to disambiguate the ambiguous words using a genetic algorithm. this proposed system has been tested on a manually sensetagged corpus of 30 most ambiguous words. wu-palmer similarity measure method has also been applied as a fitness function to the algorithm and found that precision is 81.25 percentage and recall is 74.28 percentage. in the future, the scalability of the proposed system can be improved by adding more ambiguous words to the assamese language. this proposed system is one target word wsd and can be extended to an all-word wsd system and will show more good results once the assamese will fully complete.