By Arturo Montejo Ráez, Luís Alfonso Ureña López (auth.), José Luis Vicedo, Patricio Martínez-Barco, Rafael Muńoz, Maximiliano Saiz Noeda (eds.)
This booklet constitutes the refereed court cases of the 4th overseas convention, EsTAL 2004, held in Alicante, Spain in October 2004.
The forty two revised complete papers awarded have been rigorously reviewed and chosen from seventy two submissions. The papers deal with present concerns in computational linguistics and monolingual and multilingual clever language processing and purposes, particularly written language research and iteration; pragmatics, discourse, semantics, syntax, and morphology; lexical assets; note feel disambiguation; linguistic, mathematical, and morphology; lexical assets; be aware feel disambiguation; linguistic, mathematical, and mental versions of language; wisdom acquisition and illustration; corpus-based and statistical language modeling; laptop translation and translation instruments; and computational lexicography; info retrieval; extraction and query answering; automated summarization; record categorization; common language interfaces; and discussion structures and overview of systems.
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Extra resources for Advances in Natural Language Processing: 4th International Conference, EsTAL 2004, Alicante, Spain, October 20-22, 2004. Proceedings
Surprisingly for us, the best result using this modified penalty factor was obtained just for unigrams. In automatic summarisation evaluations, unigrams have been found also to work better than n-grams in some cases [11, 12]. 4 Extensions Proposed There are a number of simple modifications to the original algorithm: 1. g. to manage and for managing. Automatic Assessment of Open Ended Questions 29 2. Removal of Closed-Class Words. These are usually important for finding matching N-grams for long values of N; however, in the case of unigrams, they are probably present in every kind of text and are not very informative.
J. L. Vicedo et al. ): EsTAL 2004, LNAI 3230, pp. 25–35, 2004. c Springer-Verlag Berlin Heidelberg 2004 26 E. Alfonseca and D. P´erez  provide a general overview of CAA tools. In previous work, we have applied BLEU  to evaluate student answers [9, 10], with surprisingly good results, considering the simplicity of the algorithm. In this paper we focus on improving the basic BLEU algorithm with different representations of the student’s text, by incorporating increasingly more complex syntactic and semantic information into our system.
The following equation illustrates the computation of OvR: OvR = Common Dur Common Dur = Dur max Dur ref + Dur auto − Common Dur (1) Dur_ref ... seg=A RefSeg Common_Dur AutoSeg ... seg=A Dur_auto ... Dur_max Fig. 2. Graphical representation of the quantities involved in the computation of the Overlap Rate Regarding the equation 1, one can realize that if, for example, a phone duration in the reference segmentation diﬀers considerably from its duration in the other segmentation, the OvR quantity takes a very small value.
Advances in Natural Language Processing: 4th International Conference, EsTAL 2004, Alicante, Spain, October 20-22, 2004. Proceedings by Arturo Montejo Ráez, Luís Alfonso Ureña López (auth.), José Luis Vicedo, Patricio Martínez-Barco, Rafael Muńoz, Maximiliano Saiz Noeda (eds.)