WORD SENSE DISAMBIUATION FOR KASHMIRI LANGUAGE USING SUPERVISED MACHINE LEARNING (2020)

Abstract: Every language used in this word has ambiguous words. The process of analyzing the word tokens and assigning the correct meanings to the ambiguous words according the context in which they are used is called word sense disambiguation(WSD). WSD is a very hot research topic in Natural Language Processing. The main purpose of my research work is to tackle the WSD problem for Kashmiri language using Supervised Machine Learning Approaches

## introduction every language spoken by people in this world contain words that have more than one meaning. meaning of such words at a particular time depends on the context in which the word has been used. the process of selecting the meaning of ambiguous word from the set of possible meanings is called word sense disambiguation @xcite . wsd is one of the hot research topics in the natural language processing (nlp) domain. for humans it seems to be very easy to understand the meaning of ambiguous words but it is a very complex problem for machines to do so. consider the following sentences in english:  this saw is blunt i saw a horror dream yesterday in the first sentence the word saw is used as noun and its meaning is a tool used to cut hard material like wood, metal. in the second sentence the word saw is past form of verb see. similarly consider the following sentence in kashmiri:  thave dare yel open the window  rache daare zeeth. keep long beard. in the above two sentences the word daare is having two different meanings. in the first sentence it means window where as in the second sentence it means beard. in nlp wsd is considered as an ai complete problem, that is, a problem whose solution presumes a solution to understanding natural language or common-sense reasoning @xcite .the meaning of an ambiguous word depends heavily on the words surrounding it. to resolve the ambiguity of words number of approaches have been designed till date and work is still going on. the research on wsd actually started in 1940's making it one of the oldest problems in the computational linguistics. some important research works for handling wsd in various languages are @xcite , @xcite , @xcite , @xcite , @xcite , @xcite , @xcite , @xcite . for resolving ambiguity in kashmiri language no work is cited. the objective of this research is to propose the wsd for kashmiri using supervised machine learning approaches. ## motivation the driving motivation for this research is that wsd is an intermediate step for the various nlp applications like machine translation, grammatical analysis, speech processing, information retrieval and hypertext navigation @xcite and developing efficient wsd system is very crucial for the better performance of these nlp applications. since there is no work cited in kashmiri language for handling wsd problem this is the first attempt in this direction, so this also motivated me for this research. the third point is that research in nlp applications for kashmiri language is in infancy stage this research will boost the research in this field and attract researchers to work in this field of artificial intelligence. ## grammar formalism and issues specific to kashmiri language grammar formalism is of great importance for creating syntactic annotation corpus and frameworks available can be categorized into two types: dependency based annotation scheme and constituency based annotation. in constituentbased annotation scheme sentence is depicted as hierarchically organized phrases and relation between and within constituents is not represented explicitly. in dependency based annotation the sentence is organized as dependency graph consisting of a head and dependent with labelled arc specifying relationship between them. kashmiri language being inflectionally rich dependency annotation scheme existing for hindi-urdu is considered suitable for annotation. but some issues needed to be addressed. these issues include v2 phenomenon, discrepancy that exists between coordinating and subordinating conjuncts, rift in complex predicates, pronominal clitics etc. @xcite . ## methodology in this study supervised machine learning approaches are to be used to handle wsd in kashmiri language. the supervised machine learning approaches work in two phases i.e, training phase and test phase. in training phase classifier is trained how to resolve ambiguity of a polysemous word (words having multiple meanings). in the testing phase the classifier assigns most appropriate sense to ambiguous word. the flowchart below depicts the methodology to be used: ## experimental outcomes the main outcomes of the study are as follows: