At the backhand, shell scripts and Perl scripts are run that compute probabilities and generate various files such as alignment file, translation table, fertility file, distoration table, etc.
#Hindi Translator Java App install#
Once you have a decent size corpora, then you need to install Giza++, Moses and IRST on your system.Develop a bilingual corpora having pretranslated sentences from source language to destination language.Setup of EBMTīasic preparation of an EBMT system requires you to do the following:
For every word, it tries to seek the dictionary translation and simply stitches the outputs into a translated sentence. When Module III also fails to translate, EBMT breaks the sentence into words. This is the last attempt by EBMT to translate the input sentence. I used Moses Decoder that uses heuristic search to effectively and efficiently find H*. I used Giza++ that measures faithfulness, Probability of an (English, Hindi) pair given a Hindi sentence and test if a given fluent sentence is a translation. I used IRST-Language Model that measures fluency and probability of Hindi sentence and provide a set of fluent sentences to test for potential translation.
Phrases like these are clubbed together to complete the sentence. This module tries to find the most probable Hindi translation of an English sentence by trying to find phrase H that would maximize P(E/H)*P(H). For example, the above English sentence is converted to This module converts the input language into tokenized format. To illustrate this example, compare the following two sentences: English follows Subject-Verb-Object (SVO) linguistic topology while Hindi follows Subject-Object-Verb (SOV) topology. Module II: Sentence Rule Based TranslationĮvery language has some grammar that describes how the words in the sentences should be organized. In the case when the input is a paragraph, then the input is first broken down into sentences, and each sentence is taken one by one and translated. If found, the corresponding Hindi sentence is retrieved and displayed as output. In this module, the input English sentence is first checked with every sentence in the available bilingual corpora for an exact match. I divided the entire EBMT system into four modules. I used the following tools in my EBMT system:
However, there are some tools that can help accelerate the process.
#Hindi Translator Java App software#
Software Usedĭeveloping your own machine translation is a difficult task. The basic premise is that, if a previously translated sentence occurs again, the same translation is likely to be correct again. This means that if an EBMT system is given a set of sentences in the source language (from which one is translating) and their corresponding translations in the target language, the system can use these examples to translate other such similar source language sentences into target language sentences.
Introduction and BackgroundĮxample based translation is essentially translation by analogy. This article is greatly inspired by the works of Ralf Brown and Balakrishnan who have done extensive research in this field. Therefore, the larger the database of pre-translated sentences, greater will be the accuracy of the EBMT system. The principle of translating in EBMT is simple: a system decides an appropriate translation of an input sentence by analyzing the pre-translated sentences in the database. In this particular case, I will be translating English sentences to Hindi. This article describes the development of Example Based Machine Translation (EBMT) system using Java on Linux platform for translation from one language to another.
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