Review of Machine Transliteration Techniques

Kaur, Kanwaljit; Singh, Parminder

doi:10.5120/18866-0061

Cited by 27 publications

(11 citation statements)

References 9 publications

(6 reference statements)

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“…Grapheme Based Approaches, Phoneme Based Approaches and mixture of these two i.e. Hybrid Approaches [11]. Grapheme based approaches can further be Rule Based, HMM (Hidden Markov Model) Based, Statistical Based and Finite State Transducers [12].…”

Section: Machine Tranliteration Approachesmentioning

confidence: 99%

An Efficient Romanization of Gurmukhi Punjabi Proper Nouns for Pattern Matching

Singh¹,

Oberoi²

2019

IJRTE

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A Romanization system is used to convert some text of a source script to the Roman script through word by word mapping. The phonological characteristics of the source word are not lost. Only writing script is changed, without any changes in the spoken language. This paper presents a rule based approach for Romanization of Gurmukhi script proper nouns. The aim is to develop a lightweight Romanization system, which may produce multiple possible results for the same input word. The algorithm uses a list of Gurmukhi script characters along with their equivalent character combinations in Roman script. Direct mapping of Gurmukhi script characters to their equivalent Roman script character combinations does not produce efficient results, so some rules are applied to get the correct mappings. The rules are basically to place or remove the letter ‘a’ in between the mapped consonants. Three different sets of rules are applied to get three different Romanized outputs. All these outputs are acceptable for information extraction using pattern matching. In Gurmukhi, some words are written differently than these are pronounced. To handle such words, these words or part of these words are stored in a database table. Along with these words their Romanized form is also stored in second column. The table is used to directly pick the Romanization from the table and use it for Romanization of these words. The result of this Romanization system is a set of possible words that can be generated from the source script word. It enables an application to pattern match those output words with some text or database to get the required information

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Section: Machine Tranliteration Approachesmentioning

confidence: 99%

An Efficient Romanization of Gurmukhi Punjabi Proper Nouns for Pattern Matching

Singh¹,

Oberoi²

2019

IJRTE

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“…Three approaches for transliteration being used are: Grapheme based models; Phoneme based models and Hybrid models [10]. Knight and Graehl (1998) have developed a phoneme based statistical model using finite state transducer that implements transformation rules to do backward transliteration.…”

Section: Related Workmentioning

confidence: 99%

Hindi to Punjabi Transliteration using Phonetic and Orthographic Rules

Kaur¹,

Singh²

2015

IJCA

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One of the important applications of Natural Language Processing is machine translation. Machine transliteration is an emerging and a very important research area in the field of machine translation. Translation systems translate message from source language to target language, keeping the exact meaning. While the transliteration system finds the same meaning word/sentence in another language, the transliteration helps us to pronounce them. This paper addresses the transliteration process from Hindi to Punjabi language using a rule based approach. Transliteration is more phonetic process, thus both source grapheme and its sound in the correct pronunciation of word have been considered for rule formation to achieve high performance and more accurate results. The system is tested on news domain text and achieved accuracy of 97%.

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“…In recent years, script transliteration has been considered as significant research in area of machine translation [9]. There are three main approaches which are used for script transliteration such as grapheme, phoneme and hybrid-based [9], [10]. A grapheme is the basic unit of written language, which includes characters (alphabets), punctuation marks, numerals and all the individual symbols of that writing language system.…”

Section: Introductionmentioning

confidence: 99%

“…The grapheme-based approaches are classified as rule-based, statistical machine transliteration, hidden markov model, finite state transducer based models, etc. [9]. A phoneme is the smallest unit of speech that distinguishes meanings.…”

Section: Introductionmentioning

confidence: 99%

GRT: Gurmukhi to Roman Transliteration System using Character Mapping and Handcrafted Rules

Singh¹,

Sachan²

2019

IJITEE

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In the last two decades, the transliteration system has got significant research attention. It is observed that Punjabi to English transliteration for all type of part-of-speech words is comparably less studied. Currently, some research work in this area is carried out but only for proper nouns and some technical terms. So, there is need to focus on all type of words. The Gurmukhi to Roman transliteration (GRT) system is the first proposed system for transliteration of all kind of part-of-speech words. This system uses the handcrafted-rules and character mapping (CM) approach for transliteration between languages involved. The CM is done for Gurmukhi script with its equivalent to Roman script. It transliterates text written in Punjabi language into English language. It is tested on 65,130 Punjabi words and achieved accuracy of 99.27%, which is better than other state-of-art system results. The developed system can be used in social media text normalization, translation, sentiment analysis of multilingual text, text summarization of multilingual text, etc.

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Review of Machine Transliteration Techniques

Cited by 27 publications

References 9 publications

An Efficient Romanization of Gurmukhi Punjabi Proper Nouns for Pattern Matching

An Efficient Romanization of Gurmukhi Punjabi Proper Nouns for Pattern Matching

Hindi to Punjabi Transliteration using Phonetic and Orthographic Rules

GRT: Gurmukhi to Roman Transliteration System using Character Mapping and Handcrafted Rules

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