Syntactic and Structural Divergence in English-to-Marathi Machine Translation

Kulkarni, Sonali B.; Deshmukh, Prapti D.; Kale, K. V.

doi:10.1109/iscbi.2013.46

Cited by 7 publications

(4 citation statements)

References 9 publications

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“…It provides the system with the morphological analysis of a Marathi sentence. The parser provides output in Shakti standard format [19], [20]. It provides the root word, POS tag, tense, gender, multiplicity, direct or oblique case, suffix, Vibhakti and other details important to identify the role of the word in the sentence.…”

Section: Methodsmentioning

confidence: 99%

Inflection rules for Marathi to English in rule based machine translation

Kharate¹,

Patil²

2021

IJ-AI

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Machine translation is important application in natural language processing. Machine translation means translation from source language to target language to save the meaning of the sentence. A large amount of research is going on in the area of machine translation. However, research with machine translation remains highly localized to the particular source and target languages as they differ syntactically and morphologically. Appropriate inflections result correct translation. This paper elaborates the rules for inflecting the parts-of-speech and implements the inflection for Marathi to English translation. The inflection of nouns, pronouns, verbs, adjectives are carried out on the basis of semantics of the sentence. The results are discussed with examples.

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Section: Methodsmentioning

confidence: 99%

Inflection rules for Marathi to English in rule based machine translation

Kharate¹,

Patil²

2021

IJ-AI

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“…For the creation of our learning base, we used excerpts from academic articles in which the application and characteristic properties of the selected DM methods are described representatively (Vainshtein et al 2018). This entails that there are two different data pools with syntactic divergence (Kulkarni et al 2013) mainly expressed in the pursued intentions and applied terminologies (cf. Table 1 for their distinction).…”

Section: Construction Of a Learning Basementioning

confidence: 99%

Intelligent User Assistance for Automated Data Mining Method Selection

et al. 2020

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In any data science and analytics project, the task of mapping a domain-specific problem to an adequate set of data mining methods by experts of the field is a crucial step. However, these experts are not always available and data mining novices may be required to perform the task. While there are several research efforts for automated method selection as a means of support, only a few approaches consider the particularities of problems expressed in the natural and domain-specific language of the novice. The study proposes the design of an intelligent assistance system that takes problem descriptions articulated in natural language as an input and offers advice regarding the most suitable class of data mining methods. Following a design science research approach, the paper (i) outlines the problem setting with an exemplary scenario from industrial practice, (ii) derives design requirements, (iii) develops design principles and proposes design features, (iv) develops and implements the IT artifact using several methods such as embeddings, keyword extractions, topic models, and text classifiers, (v) demonstrates and evaluates the implemented prototype based on different classification pipelines, and (vi) discusses the results' practical and theoretical contributions. The best performing classification pipelines show high accuracies when applied to validation data and are capable of creating a suitable mapping that exceeds the performance of joint novice assessments and simpler means of text mining. The research provides a promising foundation for further enhancements, either as a stand-alone intelligent assistance system or as an add-on to already existing data science and analytics platforms.

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“…S. B. Kulkarni [2] discuss syntactic and structural divergence issues in English-Marathi machine translation and the same translation pair is then examined for reverse translation so as to examine the nature of the divergence in each case. R.K.sinha [3] discuss different types of translation divergences in Hindi and English MT. G.V.Garje [4] describes the differences between the languages English and Marathi from a Machine Translation point of view and also encountered challenges while attempting to build a Machine Translation system from English to Marathi using Rule based Machine Translation approach.…”

Section: Related Workmentioning

confidence: 99%

Handling Challenges in Rule Based Machine Translation from Marathi to English

Kharate¹,

Patil²

2019

IJNLC

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Machine translation is being carried out by the researchers from quite a long time. However, it is still a dream to materialize flawless Machine Translator and the small numbers of researchers has focussed at translating Marathi Text to English. Perfect Machine Translation Systems have not yet been fully built owing to the fact that languages differ syntactically as well as morphologically. Majority of the researchers have opted for Statistical Machine translation whereas in this paper we have addressed the challenges of Rule based Machine Translation. The paper describes the major divergences observed in language Marathi and English and many challenges encountered while attempting to build machine translation system form Marathi to English using rule based approach and rules to handle these challenges. As there are exceptions to the rules and limit to the feasibility of maintaining knowledgebase, the practical machine translation from Marathi to English is a complex task.

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Syntactic and Structural Divergence in English-to-Marathi Machine Translation

Cited by 7 publications

References 9 publications

Inflection rules for Marathi to English in rule based machine translation

Inflection rules for Marathi to English in rule based machine translation

Intelligent User Assistance for Automated Data Mining Method Selection

Handling Challenges in Rule Based Machine Translation from Marathi to English

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