1. Articles in category: Machine Translation

    7753-7776 of 8022 « 1 2 ... 321 322 323 324 325 326 327 ... 333 334 335 »
    1. Automatic induction of bilingual resources from aligned parallel corpora: application to shallow-transfer machine translation

      Abstract  The availability of machine-readable bilingual linguistic resources is crucial not only for rule-based machine translation but also for other applications such as cross-lingual information retrieval. However, the building of such resources (bilingual single-word and multi-word correspondences, translation rules) demands extensive manual work, and, as a consequence, bilingual resources are usually more difficult to find than “shallow” monolingual resources such as morphological dictionaries or part-of-speech taggers, especially when they involve a less-resourced language. This paper describes a methodology to build automatically both bilingual dictionaries and shallow-transfer rules by extracting knowledge from word-aligned parallel corpora processed with shallow monolingual resources (morphological ...
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    2. 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
    3. 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
    4. 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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    5. 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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    6. 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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    7. 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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    8. 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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    7753-7776 of 8022 « 1 2 ... 321 322 323 324 325 326 327 ... 333 334 335 »
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