Many words in natural languages have multiple meanings. This system used the IBM models to align the texts at word level. A parallel corpus becomes very useful when the texts in the two languages are aligned. A good way to develop such a corpus is to start from available resources containing the translations from the source language to the target language. Building a parallel corpus manually is a very tedious and time-consuming task. Email: and word sense disambiguation, they are not yet available for many languages of the world. I got M.C.Sc from University of Computer Studies, Mandalay in 1997. University of Computer Studies, Yangon, Myanmar, PH-0973176035 Khin Thandar Nwet is currently pursuing Ph.D degree program in Although parallel corpora are very useful resources for many natural languages processing applications such as building machine translation systems, multilingual A parallel corpus is a collection of texts in two languages, one of which is the translation equivalent of the other. In this paper we de- scribe our efforts in building an English-Myanmar aligned parallel corpus. Research in language technologies has therefore not progressed much. Corpora and other lexical resources are not yet widely available in Myanmar. This paper deals with the step of word alignment. The second stage is called translation modeling and it includes the step of finding the word align- ments induced over a sentence aligned bilingual (parallel) corpus.
![english to myanmar language translation english to myanmar language translation](https://3.imimg.com/data3/DS/IF/MY-958174/burmese-language-to-english-translation-services-india-250x250.jpg)
For this stage, monolingual corpora of the SL and the TL are required. Separate language models are built for the source language (SL) and the target language (TL). One simple and very old but still quite useful approach for language modeling is n-gram modeling. Bilingual word alignment is the first step of most current approaches to Statistical Machine Translation or SMT. This cause prob- lem for Machine Translation, Information Retrieval, Text Summarization and many other Natural Language Processing. ROCESSING Myanmar texts is difficult in its compu- tation because sentences in Myanmar texts are represented as strings of Myanmar characters with- out spaces to indicate word boundaries. Index Terms - EM Algorithm, IBM Models, Machine Translation, Word-aligned Parallel Corpus, Natural Language Processing For the dictionary lookup approach, the proposed system uses the bilingual Myanmar-English Dictionary. The corpus based approach is based on the first three IBM models and Expectation Maximization (EM) algorithm. The proposed approach is combination of corpus based approach and dictionary lookup approach. A parallel corpus is a collection of texts in two languages, one of which is the translation equivalent of the other.Thus, the main purpose of this system is to construct word-aligned parallel corpus to be able in Myanmar-English machine translation. Since word alignment research on Myanmar and English languages is still in its infancy, it is not a trivial task for Myanmar-English text. Essential for building parallel corpora is the alignment of translated segments with source segments.
![english to myanmar language translation english to myanmar language translation](https://gtelocalize.com/wp-content/uploads/2019/11/6.-Burmese.gif)
In this paper, we describe an alignment system that aligns English-Myanmar texts at word level in parallel sentences. International Journal of Scientific & Engineering Research Volume 2, Issue 9, September-2011 1Ībstract - Word alignment in bilingual corpora has been an active research topic in the Machine Translation research groups. Building Bilingual Corpus based on Hybrid Approach for Myanmar-English Machine Translation