Language model

Results: 4719



#Item
21DISCRIMINATIVE LANGUAGE MODEL ADAPTATION FOR MANDARIN BROADCAST SPEECH TRANSCRIPTION AND TRANSLATION X. A. Liu, W. J. Byrne, M. J. F. Gales, A. de Gispert, M. Tomalin, P. C. Woodland & K. Yu Cambridge University Engineer

DISCRIMINATIVE LANGUAGE MODEL ADAPTATION FOR MANDARIN BROADCAST SPEECH TRANSCRIPTION AND TRANSLATION X. A. Liu, W. J. Byrne, M. J. F. Gales, A. de Gispert, M. Tomalin, P. C. Woodland & K. Yu Cambridge University Engineer

Add to Reading List

Source URL: mi.eng.cam.ac.uk

Language: English - Date: 2010-10-27 17:51:51
    22C ONTRIBUTED RESEARCH ARTICLE  248 A Tidy Data Model for Natural Language Processing using cleanNLP

    C ONTRIBUTED RESEARCH ARTICLE 248 A Tidy Data Model for Natural Language Processing using cleanNLP

    Add to Reading List

    Source URL: journal.r-project.org

    - Date: 2018-01-29 07:20:14
      23Language-based abstractions for dynamical systems Andrea Vandin IMT School for Advanced Studies Lucca, Lucca, Italy   Ordinary differential equations (ODEs) are the primary means to model dynamic

      Language-based abstractions for dynamical systems Andrea Vandin IMT School for Advanced Studies Lucca, Lucca, Italy Ordinary differential equations (ODEs) are the primary means to model dynamic

      Add to Reading List

      Source URL: qapl17.doc.ic.ac.uk

      - Date: 2017-04-20 09:16:43
        24MODEL SCHOOL DISTRICT POLICY ON SUICIDE PREVENTION Model Language, Commentary, and

        MODEL SCHOOL DISTRICT POLICY ON SUICIDE PREVENTION Model Language, Commentary, and

        Add to Reading List

        Source URL: education.alaska.gov

        - Date: 2017-10-10 16:34:43
          25MoCHi: Software Model Checker for a Higher-Order Functional Language

          MoCHi: Software Model Checker for a Higher-Order Functional Language

          Add to Reading List

          Source URL: www2.ims.nus.edu.sg

          - Date: 2016-09-29 23:34:21
            26Grouping Language Model Boundary Words to Speed K–Best Extraction from Hypergraphs Kenneth Heafield∗,† Philipp Koehn∗ Alon Lavie† ∗

            Grouping Language Model Boundary Words to Speed K–Best Extraction from Hypergraphs Kenneth Heafield∗,† Philipp Koehn∗ Alon Lavie† ∗

            Add to Reading List

            Source URL: aclweb.org

            - Date: 2013-05-18 12:41:37
              27A Hierarchical Bayesian Language Model based on Pitman-Yor Processes Yee Whye Teh School of Computing, National University of Singapore, 3 Science Drive 2, Singapore.

              A Hierarchical Bayesian Language Model based on Pitman-Yor Processes Yee Whye Teh School of Computing, National University of Singapore, 3 Science Drive 2, Singapore.

              Add to Reading List

              Source URL: www.gatsby.ucl.ac.uk

              - Date: 2010-06-29 13:51:12
                28A model of population dynamics applied to phonetic change James Kirby () School of Philosophy, Psychology, and Language Sciences, University of Edinburgh, Edinburgh EH8 9AD UK Morgan Sonderegger (morgan.s

                A model of population dynamics applied to phonetic change James Kirby () School of Philosophy, Psychology, and Language Sciences, University of Edinburgh, Edinburgh EH8 9AD UK Morgan Sonderegger (morgan.s

                Add to Reading List

                Source URL: mindmodeling.org

                - Date: 2013-07-15 14:53:24
                  29SIFMA Municipal Advisor Model Language:Model Independent Registered Municipal Advisor Exemption Language

                  SIFMA Municipal Advisor Model Language:Model Independent Registered Municipal Advisor Exemption Language

                  Add to Reading List

                  Source URL: cdn.portofportland.com

                  - Date: 2015-07-27 17:35:44
                    30Practice Questions for CS410 Midterm Exam Please feel free to discuss these questions with your classmates 1. Let M be the unigram language model representing the “text mining topic” shown on slide 22 of the NLP lect

                    Practice Questions for CS410 Midterm Exam Please feel free to discuss these questions with your classmates 1. Let M be the unigram language model representing the “text mining topic” shown on slide 22 of the NLP lect

                    Add to Reading List

                    Source URL: sifaka.cs.uiuc.edu

                    - Date: 2014-04-10 17:07:22