1. Association for Computational Linguistics

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    1. Mentioned In 531 Articles

    2. New Ranking Algorithms for Parsing and Tagging: Kernels overDiscrete Structures, and the Voted Perceptron

      New Ranking Algorithms for Parsing and Tagging: Kernels over Discrete Structures, and the Voted Perceptron Michael Collins AT&T Labs-Research, Florham Park, New Jersey. mcollins@research.att.com Nigel Duffy iKuni Inc., 3400 Hillview Ave., Building 5, Palo Alto, CA 94304. nigeduff@cs.ucsc.edu Abstract This paper introduces new learning algorithms for natural language processing based on the perceptron algorithm. We show how the algorithms can be efficiently applied ...
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    3. Discriminative language modeling with conditional random fields andthe perceptron algorithm.

      Discriminative Language Modeling with Conditional Random Fields and the Perceptron Algorithm Brian Roark Murat Saraclar AT&T Labs - Research {roark,murat}@research.att.com Michael Collins Mark Johnson MIT CSAIL Brown University mcollins@csail.mit.edu Mark Johnson@Brown.edu Abstract This paper describes discriminative language modeling large vocabulary speech recognition task. We trast parameter estimation methods: perceptron algorithm, a method based conditional random fields (CRFs). models enco
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    4. Max-Margin Parsing.

      Max-Margin Parsing Ben Taskar Computer Science Dept. Stanford University btaskar@cs.stanford.edu Dan Klein Computer Science Dept. Stanford University klein@cs.stanford.edu Michael Collins CS and AI Lab MIT mcollins@csail.mit.edu Daphne Koller Computer Science Dept. Stanford University koller@cs.stanford.edu Christopher Manning Computer Science Dept. Stanford University manning@cs.stanford.edu Abstract We present a novel discriminative approach to parsing inspired by the large-margin ...
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    5. 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 ...
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    6. Learning to Map Sentences to Logical Form: StructuredClassification with Probabilistic Categorial Grammars.

      Learning to Map Sentences to Logical Form: Structured Classification with Probabilistic Categorial Grammars Luke S. Zettlemoyer and Michael Collins MIT CSAIL lsz@csail.mit.edu, mcollins@csail.mit.edu Abstract This paper addresses the problem of mapping natural language sentences to lambda–calculus encodings of their meaning. We describe a learning algorithm that takes as input a training set of sentences labeled with expressions in the lambda calculus. The algorithm ...
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    7. Discriminative n-gram language modeling

      ^ star, open Brian Roark^a^, ^Corresponding Author Contact Information ^, ^1^, ^E-mail The Corresponding Author , Murat Saraclar^b^, ^1^, ^E-mail The Corresponding Author and Michael Collins^c^, ^E-mail The Corresponding Author ^aCenter for Spoken Language Understanding, OGI School of Science and Engineering at Oregon Health and Science University, 20000 NW Walker Road, Beaverton, OR 97006, United States ^bBoğaziçi University, 34342 Bebek, Istanbul, Turk
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    8. Online Learning of Relaxed CCG Grammars for Parsing to LogicalForm.

      Online Learning of Relaxed CCG Grammars for Parsing to Logical Form Luke S. Zettlemoyer and Michael Collins MIT CSAIL lsz@csail.mit.edu,mcollins@csail.mit.edu Abstract We consider the problem of learning to parse sentences to lambda-calculus representations of their underlying semantics and present an algorithm that learns a weighted combinatory categorial grammar (CCG). A key idea is to introduce non-standard CCG combinators that relax certain parts of ...
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    9. 451-465 of 531 « 1 2 ... 28 29 30 31 32 33 34 35 36 »
  1. Categories

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      Discourse, Entailment, Machine Translation, NER, Parsing, Segmentation, Semantic, Sentiment, Summarization, WSD
  2. About Association for Computational Linguistics

    The Association for Computational Linguistics (ACL) is the international scientific and professional society for people working on problems involving natural language and computation. An annual meeting is held each summer in locations where significant computational linguistics research is carried out. It was founded in 1962, but it was then named Association for Machine Translation and Computational Linguistics (AMTCL). It became the ACL only in 1968.

    The ACL has a European and a North American chapter.

    The ACL journal, Computational Linguistics, continues to be the primary forum for research on computational linguistics and natural language processing. Since 1988, the journal has been published for the ACL by MIT Press.

    The ACL book series, Studies in Natural Language Processing, is published by Cambridge University Press."