VEA Model in Word Formation Process of Maithili MT (original) (raw)
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A Suffix Based Morphological Analysis of Assamese Word Formation
International Journal on Recent and Innovation Trends in Computing and Communication, 2017
Languages have several important features such as part-of-speech, tenses, prefixes and suffixes etc. which play major roles to solve the purpose of the language. In Assamese language suffixation is a very sensitive and unavoidable factor in the formation of Assamese words. Suffixes are letters or group of letters placed right after the nouns, pronouns, adjectives, verbs and adverbs etc to intensify the meaning contextually of the newly formed words due to suffixation. Because of the inflectional nature of suffixation, it often creates new words differing in part-of-speech and meaning from the original words, it is attached with. Hence suffixation is morphodyanmic process through which new words are generated from old words changing their forms, function and meaning thus increasing the lexical inventory of Assamese language. This particular study can create a theoretical base about the nature of lexical generativity of suffixes in the formation of Assamese words.
The Analysis of Word Structure Viewed from English Morphological Perspective
2015
This article proposes a workable, teachable, generalisable as well as communicatively efficient framework for analyzing the word in English Morphology. It is proposed that a framework of English Morphology should include the understanding of word structure, morphemes and their arrangement in forming words. Further, this article explains why is morphology studied and how is morphology studied to EFL students from the point of view of several morphologists in some sources. This is also intoducing some principles used in Morphology which are taken from Eugene Nida's: 1965 textbook Morphology. Finally, the main objective of teaching morphology is to assign meaning to parts of words.
Morphological Analysis of Manipuri Language
Communications on Applied Electronics, 2017
Morphological analysis forms the basic foundation in NLP applications including syntax parsing Machine Translation (MT), Information Retrieval (IR) and automatic indexing in all languages. It is the field of the linguistics; it can provide valuable information for computer based linguistics task such as lemmatization and studies of internal structure of the words. Computational Morphology is the application of morphological rules in the field of computational linguistics, and it is the emerging area in AI, which studies the structure of words, are formed by combining smaller units of linguistics information, called morphemes: the building blocks of words. Morphological analysis provides about semantic and syntactic role in a sentence. It analyzes the Manipuri word forms and produces several grammatical information associated with the words. The Morphological Analyzer for Manipuri has been tested on 3500 Manipuri words in Shakti Standard format (SSF) using Meitei Mayek as source; thereby an accuracy of 80% has been obtained on a manual check.
Natural Language Generation (NLG) essentially requires morphsynthesizer for morphologically complex languages, especially South-Asian languages Krishnamurti, Masica and Sinha 1986]. A robust morph-synthesizer is able to raise the accuracy of generation. To build a morph-synthesizer for a language one has to take care of the morphological peculiarities of that language, specifically in Machine Translation (MT). In this paper, we describe our work on rule based Bangla morphsynthesizer. Here we have concentrated only on the synthesis of the Nouns. Noun synthesis in Bangla depends upon the feature (animate, inanimate) and demands of the noun on the semantic account and behaviour of the noun-endings. Based on these peculiarities we have given rules for building the synthesizer. We have also shown the result of the synthesizer and how it affected the accuracy.
Morphology: Indian Languages and European Languages
Natural Language Processing (NLP) is a very popular and research area of computer science. NLP is a part of Artificial Intelligent but NLP has combination of many fields such as Hindi, English, and Computer Science etc. This paper contains how verb work in Hindi and English languages and morphology of both languages. Morphological Analyzer and generator is a tool for analyzing the given word and generator for generating word given the stem and its features. There are many Indian languages and many European languages but generally Hindi language consider as an Indian language and English as a European language. Both languages have grammatical rules. In English language, we do not use verbs as gender identification but in Hindi we use verbs for gender identification.
Morphological Analysis for Manipuri Nominal Category Words with Finite State Techniques
The paper presents the design and the implementation of a morphological analyzer for Manipuri nominal category words. A method for the analysis of nominal category Manipuri words with a suffix stripping approach in a right to left direction without using any lexicon has been proposed. Manipuri being an agglutinative language and its rule-based nature while morpheme concatenation allows the morphotactics of the different available word forms to be modeled with finite state machines (FSMs). Also the very feature of the word classes which possess the characteristics that they can only be attached with affixes meant for that class only make it possible to analyze a nominal word without a lexicon. This paper discusses the morphological features of Manipuri nominal category, identifying the affixes for this class, and the steps of the new methodology to develop the FSM for nominal category to represent the morphotactics of the language, converting the FSM from non-deterministic finite automata (NFA) to deterministic finite automata (DFA) and thereby cooperating the analysis.
Sandhi: The Rule Based Word Formation in Hindi
Natural Language processing (NLP) helps a machine to understand the human language. Due to various reasons human language identification and analysis is a very tedious task. One of them is meaning of the words. In NLP, to derive meaning from a sentence, words are treated as data. Therefore, the formation of words is important for NLP. Out of 447 languages, 22 are official languages in India. Hindi being the most popular and used, became the target choice for computerization. Sandhi is a process through which two or more independent words are joined to produce a new meaningful word. In this paper we present an algorithm that performs Sandhi and does Sandhi-Vichchhed (splitting compound words). The algorithm has been tested on 887 unique Hindi words that are compound i.e. Sandhi-Vichchhed can be applied to them.
Morphology Analyser for Affix Stacking Languages: a case study in Marathi
In this paper we describe and evaluate a Finite State Machine (FSM) based Morphological Analyzer (MA) for Marathi, a highly inflectional language with agglutinative suffixes. Marathibelongsto the Indo-European familyand isconsiderably influencedby Dravidian languages. Adroit handling of participial constructions and other derived forms (Krudantasand Taddhitas) in addition to inflected forms is crucial to NLP and MT of Marathi. We first describe Marathi morphological phenomena, detailing the complexities of inflectional and derivational morphology, and then go into the construction and working of the MA. The MA produces the root word and the features. A thorough evaluation against gold standard data establishes the efficacy of this MA.To the best of our knowledge, this work is the first of its kind on a systematic and exhaustive study of the Morphotactics of a suffix-stacking language, leading to high quality morph analyzer. The system forms part of a Marathi-Hindi transfer based machine translation system. The methodology delineated in the paper can be replicated for other languages showing similar suffix stacking behaviour as Marathi.