Using IndoWordNet for Contextually Improved Machine Translation of Gujarati Idioms

Abstract

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.

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

“…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.…”

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

“…Zankhana and Sajja [24] also worked on the issue of word sense disambiguation in the Gujarati language using a Knowledge-Based Approach and a Genetic Algorithm. Using the IndoWordNet, Modh and Saini attempted to contextually improve Gujarati machine translation [25]. They used the n-gram model and tests with varying frame sizes to try to enhance the translation of Gujarati idioms.…”

“…Following an extensive literature review and identifying a pressing need of the hour while conducting research on metadata generation for Hindi poetry [26], [27] and a few other research works related to Hindi poetry [28] and Gujarati idioms [29] using WordNet, the findings indicate the need for a systematic approach to dealing with loanwords, missing words, and recently developed terminologies. Since there are so many dialects in Hindi and Gujarati, resolving such difficulties is more complicated; Intriguingly, the authors acknowledged this research as a challenging one.…”