GIdTra: A dictionary-based MTS for translating Gujarati bigram idioms to English

Saini, Jatinderkumar R.; Modh, Jatin C.

doi:10.1109/pdgc.2016.7913143

Cited by 10 publications

(6 citation statements)

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“…Identification of all *Corresponding Author www.ijacsa.thesai.org Gujarati idiom phrases is concentrated here. Surrounding words are important for the idioms having more than one literal meaning in Gujarati language [6][7][8][9]. But the dynamic generation, as well as identification of all Gujarati idiom forms from the base form of the idiom, are focused here only.…”

Section: B Gujarati Idiomsmentioning

confidence: 99%

Dynamic Phrase Generation for Detection of Idioms of Gujarati Language using Diacritics and Suffix-based Rules

Modh¹,

Saini²

2021

IJACSA

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Gujarati is the language used for everyday communication in the state of Gujarat, India. The Gujarati language is also officially recognized by the constitution and the government of India. Gujarati script is based on the Devanagari script. An idiom is an expression, phrase, or word that has a different meaning from the literal meaning of the words in it. Idioms represent the cultural heritage of Gujarati language. Idioms are used in Gujarati language for effective communication and convey of an accurate message. No Machine Translation System does the accurate translation of Gujarati idioms to English or any other language. Different idiom phrases can be generated by adding diacritic(s) as well as suffix to the root or base form of the idiom. Many forms of single idiom make automatic idiom identification as well as machine translation more challenging. This paper focuses on the design and implementation of diacritics and suffix-based rules for dynamic phrase generation and detection of idioms of Gujarati language. This implementation helps in identifying Gujarati idiom present in any possible form in the Gujarati text. The obtained results with the execution of 7050 different Gujarati idiom phrases yield an accuracy of 99.73%. The results are encouraging enough to make the proposed implementation useful for Natural Language processing tasks related to Gujarati language idioms.

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Section: B Gujarati Idiomsmentioning

confidence: 99%

Dynamic Phrase Generation for Detection of Idioms of Gujarati Language using Diacritics and Suffix-based Rules

Modh¹,

Saini²

2021

IJACSA

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“…In the database, only bigram and trigram idioms having more than one-meaning are shown. Researchers had already experimented with single meaning idioms [20][21][22]. "Idiom" field stores the bigram/ trigram/n-gram idiom.…”

Section: Algorithmmentioning

confidence: 99%

Using IndoWordNet for Contextually Improved Machine Translation of Gujarati Idioms

Modh¹,

Jatinderkumar²

2021

IJACSA

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Gujarati language is the Indo-Aryan language spoken by the Gujaratis, the people of the state of Gujarat of India. Gujarati is the one of the 22 official languages recognized by the Indian government. Gujarati script was adopted from Devanagari script. Approximately 3000 idioms are available in Gujarati language. Machine translation of any idiom is the challenging task because contextual information is important for the translation of a particular idiom. For the translation of Gujarati idioms into English or any other language, surrounding contextual words are considered for the translation of specific idiom in the case of ambiguity of the meaning of idiom. This paper experiments the IndoWordNet for Gujarati language for getting synonyms of surrounding contextual words. This paper uses n-gram model and experiments various window sizes surrounding the particular idiom as well as role of stop-words for correct context identification. The paper demonstrates the usefulness of context window in case of ambiguity in the meaning identification of idioms with multiple meanings. The results of this research could be consumed by any destination-independent machine translation system for Gujarati language.

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“…For text analysis of Indian languages, Punjabi has been explored through stop words identification [22] and categorization [23] as well as poetry corpus creation [24] and classification [25][26]. Gujarati has been, similarly explored through diacritic extraction technique [27], information retrieval [28], stop words identification [29] and categorization [30], Machine Translation System (MTS) [31][32] and classification [33]. Sanskrit has been explored through stop word generation [34] and analysis [35], bilingual dictionary [36], constituency mapper [37], lemmatizer development [38] and comparison of its morphological analyzers [39].…”

Section: Literature Reviewmentioning

confidence: 99%

Marathi Text Analysis using Unsupervised Learning and Word Cloud

Bafna¹,

Saini²

2020

IJEAT

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Managing a large number of textual documents is a critical and significant task and supports many applications ranging from information retrieval to clustering search engine results. Marathi is one of the oldest of the regional languages in the Indo-Aryan language family, dating from about AD 1000. Abundance of Marathi literature has generated a big corpus and need of summarization of information. The objective of this study is to overcome the scalability problem while managing the documents and summarize the Marathi corpus by extracting tokens. The work is better in terms of scalability and supports the consistent quality of cluster for incremental data set. Most of the past and contemporary research works have targeted English corpus document management. Marathi corpus has been mostly exploited by the researchers for exploring stemming, single-document summarization and classifier design on Marathi corpus. Implementing unsupervised learning on the Marathi corpus for summarization of multiple documents through Word Cloud is still an untouched area. Technically speaking, the current work is an application of TF-IDF, cosine-based document similarity measures and cluster dendrograms, in addition to various other Natural Language Processing (NLP) activities. Entropy and precision are used to evaluate the experiments carried on different datasets and results prove the robustness of the proposed approach for Marathi Corpus.

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GIdTra: A dictionary-based MTS for translating Gujarati bigram idioms to English

Cited by 10 publications

References 0 publications

Dynamic Phrase Generation for Detection of Idioms of Gujarati Language using Diacritics and Suffix-based Rules

Dynamic Phrase Generation for Detection of Idioms of Gujarati Language using Diacritics and Suffix-based Rules

Using IndoWordNet for Contextually Improved Machine Translation of Gujarati Idioms

Marathi Text Analysis using Unsupervised Learning and Word Cloud

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