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Dice wx wy 2n wx wy where n wx and n wy are the number of bigram occurrences in word wx and wy respectively and n wx wy represents the number of bigram occurrences found in both words. A the grapheme-based transliteration model and the phoneme-based model Bilac and Tanaka 2004 Lehal and Saini 2012a. As analyzed in Halpern 2002 there are a number of orthographic variation patterns Table 2 shows an example of okurigana variants and kun homophones. Japanese is written in four different sets of...
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How to fill out exploiting parallel corpus for

01
Step 1: Choose a parallel corpus that is relevant to your needs.
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Step 2: Ensure that the corpus consists of aligned sentences in multiple languages.
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Step 3: Determine the purpose for exploiting the parallel corpus, such as machine translation, language learning, or language model training.
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Step 4: Familiarize yourself with the corpus format and structure.
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Step 5: Preprocess the parallel corpus by removing any irrelevant information or noisy data.
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Step 6: Tokenize the sentences in each language to prepare them for further processing.
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Step 7: Align the parallel sentences to establish the correspondence between them.
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Step 8: Perform any necessary linguistic analysis or annotation on the parallel corpus.
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Step 9: Choose appropriate tools or libraries for further processing, such as training a machine translation model or building a language model.
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Step 10: Apply the desired exploitation technique to make use of the parallel corpus effectively.

Who needs exploiting parallel corpus for?

01
Researchers in the field of natural language processing (NLP) who are working on machine translation or language-related tasks.
02
Language enthusiasts or students who want to practice and improve their language skills.
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Developers who are building language-related applications or tools.
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Companies or organizations in need of high-quality parallel or bilingual data for various language-related projects.
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Academics or researchers who are studying multilingualism and language acquisition.
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Exploiting parallel corpus is used for training machine translation models and improving translation accuracy.
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Exploiting parallel corpus can be filled out by providing parallel texts in source and target languages, aligning sentences, and cleaning the data for training purposes.
The purpose of exploiting parallel corpus is to enhance the quality of machine translation outputs and facilitate cross-lingual information retrieval.
The information to be reported on exploiting parallel corpus includes language pairs, size of the corpus, cleaning and preprocessing methods, and any specific licensing agreements.
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