A Parser for Sinhala Language - First Step Towards English to Sinhala Machine Translation

Hettige, B; Karunananda, AS

doi:10.1109/iciis.2006.365795

Cited by 10 publications

(2 citation statements)

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“…BEES system consists of seven modules namely English morphological analyzer [18], English parser [19], English to Sinhala translator, Sinhala syntax generator, Sinhala morphological generator and Transliteration module [20]. English to Sinhala MT system has been implemented with the use of SWI-Prolog, Java and Prolog Server Pages (PSP).…”

Section: Existing English To Sinhala Machine Translation Systemsmentioning

confidence: 99%

A multi-agent solution for managing complexity in english to sinhala machine translation

Hettige¹,

Karunananda²,

Rzevski³

2016

Int. J. DNE

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Machine translation turns out to be an inherently complex process requiring serious attention to morphological, syntactic and semantic complexity within both the source and the target languages. Most of the existing approaches to machine translation (MT) circumvent the complexity with the assumption that morphological, syntactic and semantic analysis can be done independently and sequentially. This has resulted in depriving us of the opportunity to use the language complexity to generate high-quality translations. In view of this, research has been conducted to develop a multi-agent systems solution for MT that uses the language complexity as an opportunity for generating a more realistic translation from English to Sinhala. This multi-agent solution primarily comprises a six-agent swarm to deliberate on morphological, syntactic and semantic concerns of the source and the target languages without being constrained to operate in a sequential manner. These agents use the ontology of corpora and dictionary of two languages. This approach is inspired by the fact that people understand a sentence by incrementally reading through words while simultaneously considering the syntax and semantics. As such, when the system progresses in identification of words one by one, both syntactical and semantic concerns are entertained up to the current point of reading. As a result, initially decided words may be changed due to the present concern of morphology, syntax and semantics. A translation system has been implemented on the multi-agent system development framework named MaSMT. Experiments show that the multi-agent solution for MT gives promising results for translating sentences of an average length and further research has been carried out to accommodate translation of long sentences.

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Section: Existing English To Sinhala Machine Translation Systemsmentioning

confidence: 99%

A multi-agent solution for managing complexity in english to sinhala machine translation

Hettige¹,

Karunananda²,

Rzevski³

2016

Int. J. DNE

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“… Authors [12] used pattern directed rule based and Example Based system the accuracy result-90% for simple and compound sentence.  Authors [13] used morphological analyser. The accuracy of the system reaches 69%.…”

Section: Indian Languages Machine Translationmentioning

confidence: 99%

Shallow Parsing and Word Sense Disambiguation Used for Machine Translation from Hindi to English Languages

Mall¹,

Jaiswal²

2017

IJIES

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This paper developed innovative algorithms such as shallow parsing and modified Lesk's algorithm to resolve the issues in Word Sense Disambiguation and performed correct translation from Hindi language to English language. Shallow parsing method is based on Hidden Markov model. We also perform an evaluation for 1657 Hindi tokens with 990 phrases for Parts of speech tagging and Chunking for given Hindi sentence as input and able to achieve Precision, Recall, F-score, Accuracy for Parts of speech tagger: Accuracy: 92.09%; precision: 84.76%; recall: 89.29%; F-score: 86.97, system accuracy for Chunk: Accuracy: 93.96%; precision: 89.33%; recall: 91.31%; F-score: 90.315%. The evaluation is performed by developing confusion matrix in which the system result of Parts of speech tagger and Chunk is compared with Gold standard date provided by IIIT Hyderabad in the summer school 2015. In this paper we discuss the second problem Word Sense Disambiguation in which we enhance the Modified Lesk algorithms by using overlap based method which will find information between three pieces of words in a given context. The system generated result resolves the issues of Word Sense Disambiguation and shows the comparison result with the website Google Translator in which we input polysemy word in a Hindi sentence and same sentence input in our generated system and shows the comparison result of both. The output result shows that our system resolves Word Sense Disambiguation and produces correct translation and Google Translator is fails to resolves the correct Translation.

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A methodology for machine translation of simple sentences from Kannada to English language

Kodabagi

Angadi

2016

2016 2nd International Conference on Contemporary Computing and Informatics (IC3I)

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A Parser for Sinhala Language - First Step Towards English to Sinhala Machine Translation

Cited by 10 publications

References 0 publications

A multi-agent solution for managing complexity in english to sinhala machine translation

A multi-agent solution for managing complexity in english to sinhala machine translation

Shallow Parsing and Word Sense Disambiguation Used for Machine Translation from Hindi to English Languages

A methodology for machine translation of simple sentences from Kannada to English language

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