1. Articles in category: Machine Translation

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    1. A Chinese Segmentation and Tagging Module Based on the Interpolated Probabilistic Model

      Chinese is a challenging language in natural language processing. Unlike other languages like English, Portuguese, the first step in Chinese text processing is the segmentation because there are no delimiters in a Chinese sentence for identifying the words boundaries in it. And there are many ambiguity problems during Chinese processing like segmentation ambiguities, unknown words problem, part-of-speech ambiguities, etc. In segmentation and tagging, one of the main tasks is to identify unknown words and recognize proper nouns. In the research, efforts are being paid on this particular problem. In this paper, an integrated application with segmentation and tagging ability has ...
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      Mentions: Macau
    2. Application of Translation Corresponding Tree (tct) Annotation Schema for Chinese to Portuguese Machine Translation

      In Example Based Machine Translation (EBMT) research, there are three main approaches: Surface Based, Pattern Based and Structure Based approach. In Structure Based EBMT system, such as SSTC approach [1], it has a problem that it relies on two syntax parsers to analyze the translation examples, but robust syntax parsers are not always available. On the other hand, Chinese and Portuguese belong to two different language families and there exist grammatical deviation problem between them. In order to resolve the weakness of the Structure Based EBMT system and linguistic problems between Chinese and Portuguese, Tang and Wong [2] propose a ...
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      Mentions: Macau
    3. Machine Learning Methods in Natural Language Processing

      Machine Learning Methods in Natural Language Processing Michael Collins MIT CSAIL Some NLP Problems Information extraction – Named entities – Relationships between entities Finding linguistic structure – Part-of-speech tagging – Parsing Machine translation Common Themes Need to learn mapping from one discrete structure to another – Strings to hidden state sequences Named-entity extraction, part-of-speech tagging – Strings to strings Machine translation – Strings to underlying trees Pa
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    4. Slides from a talk given at Conll 2006

      An SVM Approach for Natural Language Learning Michael Collins MIT EECS/CSAIL Joint work with Peter Bartlett, David McAllester, Ben Taskar Supervised Learning in NLP • Goal is to learn a function F : X → Y , where X is a set of possible inputs, Y is a set of possible outputs. • We have a training sample (x1 , y1 ), (x2 , y2 ), . . . , (xn , yn ) where each (xi , yi ) ∈ X × Y E.g., each xi is a sentence, each yi is a gold-standard parse Global Linear Models • Three components: GEN is a func
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    5. Hidden-Variable Models for Discriminative Reranking.

      Hidden–Variable Models for Discriminative Reranking Terry Koo MIT CSAIL maestro@mit.edu Michael Collins MIT CSAIL mcollins@csail.mit.edu Abstract We describe a new method for the representation of NLP structures within reranking approaches. We make use of a conditional log–linear model, with hidden variables representing the assignment of lexical items to word clusters or word senses. The model learns to automatically make these assignments based on a discriminative training criterion. Trainin
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    6. Clause Restructuring for Statistical Machine Translation.

      Clause Restructuring for Statistical Machine Translation Michael Collins MIT CSAIL mcollins@csail.mit.edu Philipp Koehn School of Informatics University of Edinburgh pkoehn@inf.ed.ac.uk Ivona Kuˇ erov´ c a MIT Linguistics Department kucerova@mit.edu Abstract We describe a method for incorporating syntactic information in statistical machine translation systems. The first step of the method is to parse the source language string that is being translated. The second step is to apply a series of
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    7. Discriminative Syntactic Language Modeling for Speech Recognition.

      Discriminative Syntactic Language Modeling for Speech Recognition Michael Collins MIT CSAIL mcollins@csail.mit.edu Brian Roark OGI/OHSU roark@cslu.ogi.edu Murat Saraclar Bogazici University murat.saraclar@boun.edu.tr Abstract We describe a method for discriminative training of a language model that makes use of syntactic features. We follow a reranking approach, where a baseline recogniser is used to produce 1000-best output for each acoustic input, and a second “reranking” model is then used
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    8. Discriminative Reranking for Natural Language Parsing.

      Discriminative Reranking for Natural Language Parsing Michael Collins and Terry Koo Massachusetts Institute of Technology This paper considers approaches which rerank the output of an existing probabilistic parser. The base parser produces a set of candidate parses for each input sentence, with associated probabilities that de ne an initial ranking of these parses. A second model then attempts to improve upon this initial ranking, using additional features of the tree as evidence. The strength
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    9. A Discriminative Model for Tree-to-Tree Translation.

      A Discriminative Model for Tree-to-Tree Translation Brooke Cowan MIT CSAIL brooke@csail.mit.edu Ivona Ku˘ erov´ c a MIT Linguistics Department kucerova@mit.edu Michael Collins MIT CSAIL mcollins@csail.mit.edu Abstract This paper proposes a statistical, treeto-tree model for producing translations. Two main contributions are as follows: (1) a method for the extraction of syntactic structures with alignment information from a parallel corpus of translations, and (2) use of a discriminative, fea
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    10. Chinese Syntactic Reordering for Statistical Machine Translation.

      Chinese Syntactic Reordering for Statistical Machine Translation Philipp Koehn Michael Collins Chao Wang MIT CSAIL School of Informatics MIT CSAIL 32 Vassar Street, Room 362 32 Vassar Street, Room G-484 2 Buccleuch Place, 5BP 2L2 Edinburgh, EH8 9LW, UK Cambridge, MA 02139, USA Cambridge, MA 02139, USA wangc@csail.mit.edu mcollins@csail.mit.edu pkoehn@inf.ed.ac.uk Abstract Syntactic reordering approaches are an effective method for handling word-order differences between source and target langua
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    11. Head-Driven Statistical Models for Natural Language Parsing

      HEAD-DRIVEN STATISTICAL MODELS FOR NATURAL LANGUAGE PARSING Michael Collins A DISSERTATION in Computer and Information Science Presented to the Faculties of the University of Pennsylvania in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy 1999 Professor Mitch Marcus Supervisor of Dissertation Professor Jean Gallier Graduate Group Chair COPYRIGHT Michael Collins 1999 Acknowledgements Mitch Marcus was a wonderful advisor. He gave consistently good advice, and all
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    12. Machine translation of online product support articles using a data-driven MT system.

      Machine translation of online product support articles using a data-driven MT system Stephen D. Richardson September 2004 Available Documents: Word 187KB At AMTA 2002, we reported on a pilot project to machine translate Microsoft's Product Support Knowledge Base into Spanish. The successful pilot has since resulted in the permanent deployment of both Spanish and Japanese versions of the knowledge base, as well as ongoing pilot projects for French and German. The translated articles in each case
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    7681-7704 of 7938 « 1 2 ... 318 319 320 321 322 323 324 ... 329 330 331 »
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