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Grammar Checker for Hindi and Other Indian Languages International Journal of Scientific & Engineering Research Volume 11, Issue 6, June-2020 1783 ISSN 2229-5518 Grammar Checker for Hindi and Other Indian Languages Anjani Kumar Ray, Vijay Kumar Kaul Center for Information and Language Engineering Mahatma Gandhi Antarrashtriya Hindi Vishwavidyalaya, Wardha (India) Abstract: Grammar checking is one of the sentence is grammatically well-formed. In most widely used techniques within absence of the potential syntactic parsing natural language processing (NLP) approach, incorrect or not-so-well applications. Grammar checkers check the grammatically formed sentences are grammatical structure of sentences based analyzed or produced. The preset paper is on morphological and syntactic an exploratory attempt to devise the hybrid processing. These two steps are important models to identify the grammatical parts of any natural language processing relations and connections of the words to systems. Morphological processing is the phrases to sentences to the extent of step where both lexical words (parts-of- discourse. Language Industry demands speech) and non-word tokens (punctuation such economic programmes doing justice marks, made-up words, acronyms, etc.) are and meeting the expectations of language analyzed into IJSERtheir components. In engineering. syntactic processing, linear sequences of words are transformed into structures that Keywords: Grammar Checking, Language show grammatical relationships among the Engineering, Syntax Processing, POS words in the sentence (Rich and Knight Tagging, Chunking, morphological 1991) and between two or more sentences Analysis joined together to make a compound or complex sentence. There are three main Introduction: Grammar Checker is an approaches/models which are widely used NLP application that helps the user to for grammar checking in a language; write correct sentence in the concerned language. It compiles the give text in such probability-based models, rule-based a manner that reports the error found after models and hybrid models. In rule-based analyzing the text at the grammatical grammar checking, each sentence is parameters of the language. Grammar checker enables you to correct the most completely parsed to check whether the unnoticeable mistakes/errors. Grammar IJSER © 2020 http://www.ijser.org International Journal of Scientific & Engineering Research Volume 11, Issue 6, June-2020 1784 ISSN 2229-5518 Checker helps the user to write better – Probability Based Models, Hindi with efficiently equipped features that corrects the texts. Grammar Checker – Rule Based Models corrects grammatical mistake based on the – Hybrid Models. context of complete sentences with unmatched accuracy. For the utilization of Methodology: The methodology is used in Grammar checking software the user has designing the grammar checker is to enter the text that is intended to be deductive. System can extract the corrected for grammar check and information related to grammar rules: punctuation mistakes the user has to just morphological, syntactic and semantic click to check error related to Grammar evaluation of each word in a given and punctuation and it will report the error sentence. The information cannot get in found in the word or phrase with a wavy one go but the information is extracted in underline. To get the possible solution to each phase of the system and the error then you have to click right hand on information is accumulated and stored it will show the possible solution for with each individual word. The grammar particular error. Most of mistake which are checker has been built on the basis of committed by the user be it related to syntactic rules the Hindi language Gender, number and/or related to observes. System converts the document agreement between phrases of sentence. into token of paragraph. The paragraph is Most of Grammar checkers have the spell further divided into sentences and each checker embedded in it. sentence is divided into token of word and non-word sequences. Using these State of Art: Grammar checking is one of techniques we are in a position to know the most widely used techniques within about each token which has its original natural languageIJSER processing (NLP) belonging to any given paragraph and applications. Grammar checker checkes sentence. the grammatical structure of sentence based on morphological and syntactic Tokenization Phase: In the process of processing. These two steps are important tokenization the text input divided in long parts of any natural language processing string of characters, into subunits, called systems. Morphological processing is the tokens[18] . Here the token means an step where both lexical words (parts-of- individual appearance of a word in certain speech) and non-word tokens (punctuation position in a given text. For instance one marks, made-up words, acronyms, etc.) are can consider (लड़कⴂ ləɽəkoň ladakon) as an analyzed into their components. In instance of word (लड़का ləɽəka ladaka). syntactic processing, linear sequences of words are transformed into structures that In a running text the token are generally show grammatical relationships among the separated by white space but token words words of a given sentence. may contain following characters as punctuation markers and other as part of Approaches/models and other frameworks, the word for e.g. widely used for grammar checking in a language are: IJSER © 2020 http://www.ijser.org International Journal of Scientific & Engineering Research Volume 11, Issue 6, June-2020 1785 ISSN 2229-5518 well. This type of ambiguity cannot be resolved by one sentence. लड़का लड़का’ लड़का,’ लड़का” लड़का. लड़का). Name Entity Recognition (NER): NER ləɽəka ləɽəka’ ləɽəka,’ ləɽəka” plays an important role in identifying the ləɽəka. ləɽəka). token word which represents one identity ladakaa ladakaa’ ladakaa,’ ladakaa” i.e. NER and it behaves like a Proper ladakaa. ladakaa). Noun. Thus NER identification process will affect overall performance of the system. For NER identification we discuss some rules which helps in identifying the Abbreviation Identification: In NER and combine group of token which is Tokenization process, we have to be very part of any NER then system can be treat carefull about using of dot (.) in defining as a one single unit or single token. sentence boundaries and it is also used in writting abbreviations. There is a need for Some Rules are as follows … writing the rules to identify abbreviations. Identification of Name of Person We can predict some abbreviations such as In Hindi language, common title that is the abbreviations are written with dot (.) used in writing the names of people such and space such as अ. ग. प. is used as as abbreviations for, असम गण परिषद (əsəm ɡəɳ pərɪʂəd̪ asama gan parishad). In this, a श्री श्रीमान श्रीमति,डॉ. इंति. मोहििमा, मैडम, sequence of letterIJSER-dot-letter-dot pattern is मैम, etc. appearing. Abbreviations are written with ʃri ʃriman ʃrimətɪ,ɖɒ. ɪnʤɪ. moɦətərəma, dot (.) and without space between ̪ ̪ mɛɖəm, mɛm alphabets such as, अ.ग.प. is used as abbreviations for असम गण परिषद (əsəm When the system encounters such title ɡəɳ pərɪʂəd̪ asama gan parishad) word it checks the next word which may be the part of name of a Person written in abbreviations are written without space fashion and without dot (.) such as अगप Is used as abbreviations for असम गण परिषद (əsəm [Title ] [First Name] [Middle Name] [Sur Name] [Honorific Marker word] ɡəɳ pərɪʂəd̪ asama gan parishad). Identification of Date String Some abbreviation are written as a group of characters without space and without Date string is being identified by using dot (.) and such word also belongs to regular expression and is consider the best lexicon of Hindi language such as आप i.e. way to do it. There are several patterns for used as abbreviations for आम आदमी पार्टी writing the date string such as (aama aadamee paartee) but as we know that आप is a very prompt Hindi pronoun as IJSER © 2020 http://www.ijser.org International Journal of Scientific & Engineering Research Volume 11, Issue 6, June-2020 1786 ISSN 2229-5518 01/02/2017, 1/2/2017, 01/02/17, 1//2/17, system Morphological Analysis is being 01-Feb-2017, Feb 01, 2017 etc. used for analyzing the word and its lexical form. Morphological Analysis of word During the identification process of data will give some information about its and time string, we have to write several Gender and Number and in case the word regular expressions to identify all types of is a verb it will give information about date and time string. Tense, Aspect and Modality (TAM). 1. Identification of Numeric Value / There are various approaches used in Currency designing the Morphological Analysis i.e. There are several words in a document like Corpus Based Approach, Stemming 200 셁, 셁 200, 셁 200/- these word will Approach, Paradigm Approach and Finite State Transducer (FST) Based approaches. represent for some currency value. Its POS In the proposed System Paradigm category will be “NCD” i.e. numerical Approach for Morphological Analysis is cardinal. used, as Hindi is a highly inflectional On other hand, some words like 9 वा, 9 वे, language. Hence, the Paradigm based 9 वी. These words contain two tokens. The approach is most efficient approach for morph analyzer for Hindi and probably for first token is a numerical value and the other Indian languages since a root word second token which is attached to previous can generate various words by adding the token gives some additional information suffix, prefix, or circumfix. related to Gender, Number etc. such as when token “वी” is added to a numerical This type of Morphological Analysis is value the word become a feminine gender done in two Phases and likewise when token “वे” is added to IJSERNominal Word Morphology any numerical token then word formed by these tokens having depict the plural Verb Identification using Morphological Number. Analysis Therefore when we analyze the tokens we POS Tagging : Part-of-speech tagging is a have to extract some information about process of assigning the part-of-speech or gender, Number and sometimes about the other lexical class marker to each word in Person.
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