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A Computational grammar of Sinhala for English-Sinhala machine translation

By B Hettige

Abstract

CD-ROM included ; A dissertation submitted to the Department of Information Technology for the degree of Master of PhilosophyCommunication is fundamental to the evolution and development of all kinds of living beings. With no disputes, languages should be recognized as the most amazing artifacts ever developed by mankind to enable communication. Computer has also become such a unique machine, due to its capacity to communicate with humans through languages. It is worth mentioning that the languages understood by computers and humans are quite different, yet people can communicate with computers. This has been possible since the computer is fundamentally an artifact that can translate one language to another. Therefore, computers must be able to do language translations than any other computing task. Nowadays, computing is evolving to enable machine-machine communication with no or little human intervention, yet humans continue to face with what is called language barrier for communication. In particular, a vast collection of world knowledge written in English has been inaccessible to communities who cannot communicate in English. Such communities are unable to contribute to the development of world knowledge due to the language barrier. As a result many people have embarked into research in computer aided natural language translation. This area is commonly known as Machine Translation. Among others, Aptium, Bable fish, Google translator, SYSTRAN, EDR, Anusaaraka, AngalaHindi, AnagalaBarathi, and Mantra are some examples for popular machine translation systems. These systems use various approaches including Human-assisted, Rule-based, Corpus-based, Knowledge-based, Hybrid and Agent-based to translate from one language to another. However, due to inherent diversifications of natural languages, a generic machine translation approach is far from reality. This thesis presents a computational grammar for Sinhala language to develop English to Sinhala machine translation system with an underlying theoretical basis. This system is known as BEES, an acronym for Bilingual Expert for English to Sinhala machine translation. The concept of Varanegeema (conjugation) in Sinhala language has been considered as the philosophical basis of this approach to the development of BEES. The Varanegeema in Sinhala language is able to handle large number of language primitives associated with nouns and verbs. For instance, Varanegeema handles the language primitives such as person, gender, tense, number, preposition and subjectivity/objectivity. More importantly, Varanegeema allows deriving all associated word forms from a given base word. This enables to drastically reduce the size of the Sinhala dictionary. Since the concept of Varanegeema can be expressed by a set of rules, it nicely goes with rule-based implementation of machine translation systems. BEES implements 85 grammar rules for Sinhala nouns and 18 rules for Sinhala verbs. BEES compresses with seven modules namely English Morphological analyzer, English Parser, English to Sinhala base word translator, Sinhala Morphological Generator, Sinhala Parser, Transliteration module and Intermediate Editor. In addition to the main modules, system comprises of four dictionaries, namely, English dictionary, Sinhala dictionary, English-Sinhala Bilingual dictionary and the Concept dictionary. BEES primarily shares the features with the Rule-based, Context-based and Human-assisted approaches to machine translation. The BEES has been implemented using Java and Swi-Prolog to run on both Linux and Windows environments. The English to Sinhala Machine Translation system, BEES has been evaluated to test the hypothesis that concepts of Varanegeema can be used to drive English to Sinhala machine translation. The English to Sinhala machine translation system has been evaluated through three steps. As the first step, all the language processing primitives such as morphological analyzers, parsers, translator and the transliteration module have been tested through the white box testing approach. In order to test each module, several online testing tools ii including English morphological analyzer, English parser and Sinhala word generator have been implemented. By using these online tools each module has been completely tested through a carefully created test plan. In addition, an online evaluation test bed has also been implemented to continuously capture feedback from online users. This online evaluation test bed gives facilities to make different types of sentences using a given set of words. Word Error Rate and the Sentence Error Rate were calculated by using these evaluation results. Finally the intelligibility and the accuracy tests have been conducted through the human support. In order to evaluate the intelligibility and the accuracy of the English to Sinhala machine translation system, following steps were followed. Two hundred sample sentences were collected and grouped into 20 sets (10 sentences per each set). Then each sentence was translated using the English to Sinhala Machine Translation system. Each set was given to the human translators and scored. The intelligibility and the accuracy were calculated through the above evaluation results. The experimental result shows that English morphological analyzer, English parser, English to Sinhala base word translator, Sinhala morphological generator and the Sinhala sentence generator successfully work with more than 90% accuracy. Overall result of the evaluation shows 89% accuracy with the word error rate of 7.2% and the sentence error rate of 5.4%. The BEES successfully translates English sentences with simple or complex subjects and objects. The translation system successfully handles most commonly used patterns of the tenses including active and passive voice forms.Communication is fundamental to the evolution and development of all kinds of living beings. With no disputes, languages should be recognized as the most amazing artifacts ever developed by mankind to enable communication. Computer has also become such a unique machine, due to its capacity to communicate with humans through languages. It is worth mentioning that the languages understood by computers and humans are quite different, yet people can communicate with computers. This has been possible since the computer is fundamentally an artifact that can translate one language to another. Therefore, computers must be able to do language translations than any other computing task. Nowadays, computing is evolving to enable machine-machine communication with no or little human intervention, yet humans continue to face with what is called language barrier for communication. In particular, a vast collection of world knowledge written in English has been inaccessible to communities who cannot communicate in English. Such communities are unable to contribute to the development of world knowledge due to the language barrier. As a result many people have embarked into research in computer aided natural language translation. This area is commonly known as Machine Translation. Among others, Aptium, Bable fish, Google translator, SYSTRAN, EDR, Anusaaraka, AngalaHindi, AnagalaBarathi, and Mantra are some examples for popular machine translation systems. These systems use various approaches including Human-assisted, Rule-based, Corpus-based, Knowledge-based, Hybrid and Agent-based to translate from one language to another. However, due to inherent diversifications of natural languages, a generic machine translation approach is far from reality. This thesis presents a computational grammar for Sinhala language to develop English to Sinhala machine translation system with an underlying theoretical basis. This system is known as BEES, an acronym for Bilingual Expert for English to Sinhala machine translation. The concept of Varanegeema (conjugation) in Sinhala language has been considered as the philosophical basis of this approach to the development of BEES. The Varanegeema in Sinhala language is able to handle large number of language primitives associated with nouns and verbs. For instance, Varanegeema handles the language primitives such as person, gender, tense, number, preposition and subjectivity/objectivity. More importantly, Varanegeema allows deriving all associated word forms from a given base word. This enables to drastically reduce the size of the Sinhala dictionary. Since the concept of Varanegeema can be expressed by a set of rules, it nicely goes with rule-based implementation of machine translation systems. BEES implements 85 grammar rules for Sinhala nouns and 18 rules for Sinhala verbs. BEES compresses with seven modules namely English Morphological analyzer, English Parser, English to Sinhala base word translator, Sinhala Morphological Generator, Sinhala Parser, Transliteration module and Intermediate Editor. In addition to the main modules, system comprises of four dictionaries, namely, English dictionary, Sinhala dictionary, English-Sinhala Bilingual dictionary and the Concept dictionary. BEES primarily shares the features with the Rule-based, Context-based and Human-assisted approaches to machine translation. The BEES has been implemented using Java and Swi-Prolog to run on both Linux and Windows environments. The English to Sinhala Machine Translation system, BEES has been evaluated to test the hypothesis that concepts of Varanegeema can be used to drive English to Sinhala machine translation. The English to Sinhala machine translation system has been evaluated through three steps. As the first step, all the language processing primitives such as morphological analyzers, parsers, translator and the transliteration module have been tested through the white box testing approach. In order to test each module, several online testing tools ii including English morphological analyzer, English parser and Sinhala word generator have been implemented. By using these online tools each module has been completely tested through a carefully created test plan. In addition, an online evaluation test bed has also been implemented to continuously capture feedback from online users. This online evaluation test bed gives facilities to make different types of sentences using a given set of words. Word Error Rate and the Sentence Error Rate were calculated by using these evaluation results. Finally the intelligibility and the accuracy tests have been conducted through the human support. In order to evaluate the intelligibility and the accuracy of the English to Sinhala machine translation system, following steps were followed. Two hundred sample sentences were collected and grouped into 20 sets (10 sentences per each set). Then each sentence was translated using the English to Sinhala Machine Translation system. Each set was given to the human translators and scored. The intelligibility and the accuracy were calculated through the above evaluation results. The experimental result shows that English morphological analyzer, English parser, English to Sinhala base word translator, Sinhala morphological generator and the Sinhala sentence generator successfully work with more than 90% accuracy. Overall result of the evaluation shows 89% accuracy with the word error rate of 7.2% and the sentence error rate of 5.4%. The BEES successfully translates English sentences with simple or complex subjects and objects. The translation system successfully handles most commonly used patterns of the tenses including active and passive voice forms

Topics: INFORMATION TECHNOLOGY - Thesis, SOFTWARE LACALISATION, MACHINE TRANSLATION, COMPUTATIONAL GRAMMAR, BILINGUAL EXPERT FOR ENGLISH TO SINHALA MACHINE TRANSLATION, BEES
Year: 2010
OAI identifier: oai:localhost:123/890
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