Hits 7.381 – 7.400 of 7.453
7381 |
Deep Learning with Constraints for Answer-Agnostic Question Generation in Legal Text Understanding
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7382 |
Text and Network Mining for Literature-Based Scientific Discovery in Biomedicine.
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7383 |
Detecting gross alignment errors in the Spoken British National Corpus
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Abstract:
The paper presents methods for evaluating the accuracy of alignments between transcriptions and audio recordings. The methods have been applied to the Spoken British National Corpus, which is an extensive and varied corpus of natural unscripted speech. Early results show good agreement with human ratings of alignment accuracy. The methods also provide an indication of the location of likely alignment problems; this should allow efficient manual examination of large corpora. Automatic checking of such alignments is crucial when analysing any very large corpus, since even the best current speech alignment systems will occasionally make serious errors. The methods described here use a hybrid approach based on statistics of the speech signal itself, statistics of the labels being evaluated, and statistics linking the two. ; Citation: Baghai-Ravary, L., Grau, S. & Kochanski, G. (2011). Detecting gross alignment errors in the Spoken British National Corpus. Presented at: New Tools and Methods for Very-Large-Scale Phonetics Research, University of Pennsylvania, January 28-31, 2011.
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Keyword:
ASR; detector; duration; hidden Markov model; HMM; HTK; linear regression; Linguistics; Natural Language Processing; Phonetics; probability; signal processing; speech recognition
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URL: http://kochanski.org/gpk/papers/2010/LargeCorpora/ http://www.ling.upenn.edu/phonetics/workshop/program.html
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7385 |
Measuring Semantic Distance using Distributional Profiles of Concepts
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7386 |
Exploiting Linguistic Knowledge to Infer Properties of Neologisms
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7387 |
Exploring neural paraphrasing to improve fluency of rule-based generation
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7388 |
Distributed prediction of relations for entities: the Easy, the Difficult, and the impossible
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7389 |
Twitter as a lifeline: human-annotated Twitter corpora for NLP of crisis-related messages
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7392 |
First version (v1) of the integrated platform/nand documentation
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7393 |
Criteria for evaluation of resources, technology and integration
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7394 |
Documentation of P clue/ lexical class from Weka computer Web Service
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7396 |
Third evaluation report. Evaluation of PANACEA v3 and produced resources
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7397 |
Final Report on the Corpus Acquisition & Annotation subsystem and its components
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7400 |
Integrated Final Version of the Components for Lexical Acquisition
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