Context Based MTS for Translating Gujarati Trigram and Bigram Idioms to English

Self Cite

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.

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

Self Cite

“…Table II shows the classification of bigram and trigram idioms on the base of meanings of idioms. It is based on the work of Modh and Saini [22]. If idiom has single meaning, then English translation of that particular idiom is very simple and direct, algorithm has to replace its meaning in the place of that idiom.…”

Section: A Data Collectionmentioning

confidence: 99%

“…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

Self Cite

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.

“…Gujarati is written in the Devanagari script as well and is currently supported by The Unicode Standard [6]. Gujarati is also thriving in terms of research and development in recent years [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Towards a Richer IndoWordNet with New Additions for Hindi and Gujarati Languages

Audichya¹,

Saini²,

Modh³

2022

Self Cite

The authors of this research paper present a mechanism for dealing with loanwords, missing words, and newly developed terms inclusion issues in WordNets. WordNet has evolved as one of the most prominent Natural Language Processing (NLP) toolkits. This mechanism can be used to improve the WordNet of any language. The authors chose to work with the Hindi and Gujarati languages in this research work to achieve a higher quality research aspect because these are the languages with major dialects. The research work used more than 5000 Hindi verse-based data corpus instead of a prose-based data corpus.As a result, nearly 14000 Hindi words were discovered that were not present in the popular Hindi IndoWordNet, accounting for 13.23 percent of the total existing word count of 105000+. Working with idioms was a distinct method for the Gujarati language. Around 3500 idioms data were used, and nearly 900 Gujarati terms were discovered that did not exist in the IndoWordNet, accounting for nearly 1.4 percent of the total of 64000+ Gujarati words in the IndoWordNet. It will also contribute almost 14000 Hindi words and around 900 Gujarati words to the IndoWordNet project.