Download Advances in natural language processing: 4th international by José Luis Vicedo, Particio Martínez-Barco, Rafael Munoz, PDF

By José Luis Vicedo, Particio Martínez-Barco, Rafael Munoz, Maximiliano Saiz Noeda

ISBN-10: 3540234985

ISBN-13: 9783540234982

This ebook constitutes the refereed complaints of the 4th foreign convention, EsTAL 2004, held in Alicante, Spain in October 2004.

The forty two revised complete papers awarded have been conscientiously reviewed and chosen from seventy two submissions. The papers handle present concerns in computational linguistics and monolingual and multilingual clever language processing and purposes, particularly written language research and new release; pragmatics, discourse, semantics, syntax, and morphology; lexical assets; be aware feel disambiguation; linguistic, mathematical, and morphology; lexical assets; be aware experience disambiguation; linguistic, mathematical, and mental versions of language; wisdom acquisition and illustration; corpus-based and statistical language modeling; desktop translation and translation instruments; and computational lexicography; info retrieval; extraction and query answering; automated summarization; rfile categorization; normal language interfaces; and discussion structures and assessment of systems.

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Additional info for Advances in natural language processing: 4th international conference, EsTAL 2004, Alicante, Spain, October 20-22, 2004: proceedings

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Fig. 2 illustrates the computation of Automatic Phonetic Alignment and Its Confidence Measures 39 this measure. Given a segment, a reference segmentation (RefSeg), and the segmentation to be evaluated (AutoSeg), OvR is the ratio between the number of frames that belong to that segment in both the segmentations (Common_Dur in the fig. 2) and the number of frames that belong to the segment in one segmentation, at least (Dur_max if the fig. 2). The following equation illustrates the computation of OvR: Fig.

A synthetic speech signal is generated with the expected phonetic sequence, together with the segment boundaries. Then, some spectral features are computed from the recorded and the generated speech signals, and finally the Dynamic Time Warping (DTW) algorithm [3] is applied to compute the aligned path between the signals for which there is a better match between the spectral features. The reference signal segment boundaries are mapped into the recorded signal using this alignment path. A comparison between the results of HMM-based and speech-synthesis-based segmentation [4] has showed that in general (about 70% of times) the speech-synthesis-based segmentation is more accurate than the HMM-based one, however, it tends to generate few large boundary errors (when it fails it fails badly).

Thesis, University of Colorado, Boulder (2000) 5. : Enriching automated essay scoring using discourse marking. In: Proceedings of the Workshop on Discourse Relations and Discourse Marking, ACL, Montreal, Canada (1998) 6. : Automated evaluation of essay and short answers. In: Proceedings of the Int. CAA Conference. (2001) 7. : An overview of current research on automated essay grading. T. Education 2 (2003) 319–330 8. : Bleu: a method for automatic evaluation of machine translation (2001) 9. : Application of the BLEU method for evaluating freetext answers in an e-learning environment.

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