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Mathematics - Applications | Foundations of Text Alignment - Statistical Machine Translation Models from Bitexts to Bigrammars

Foundations of Text Alignment

Statistical Machine Translation Models from Bitexts to Bigrammars

Wu, Dekai

2015, X, 90 p.

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ISBN 978-3-642-25844-2

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  • The book provides a systematic, foundational introduction to automatic alignment of parallel texts
  • It surveys a wide variety of fundamental alignment techniques including: IBM and HMM alignment models, techniques for aligning comparable corpora and learning of phrasal bilexicons, more recent alignment techniques such as greedy/competitive approaches and LTG models
  • Useful for both practitioners and researchers in machine translation, natural language processing, bilingual lexicography, and computer assisted language learners
This book provides a systematic, foundational introduction to automatic alignment of parallel texts, a family of essential corpus analysis techniques for computing and learning the mappings between corresponding parts of the texts. Bitext alignment lies at the heart of all data-driven machine learning approaches to automatic translation, and the rapid research progress on alignment during the past two decades underlies the success of statistical machine translation approaches.  Alignment is used across a wide range of resource acquisition applications including word sense disambiguation, terminology extraction, and grammar induction, as well as in translation memories and biconcordances for translators' assistants, bilingual lexicographers, and computer assisted language learners.

Content Level » Graduate

Keywords » bilingual lexicography - computer assisted language learning - grammar induction - machine learning - statistical machine translation - terminology extraction - word sense disambiguation

Related subjects » Applications - Artificial Intelligence - Computer Science - Theoretical Computer Science

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