Text, Speech and Dialogue: 17th International Conference, by Petr Sojka, Aleš Horák, Ivan Kopeček, Karel Pala

By Petr Sojka, Aleš Horák, Ivan Kopeček, Karel Pala

This booklet constitutes the refereed court cases of the seventeenth overseas convention on textual content, Speech and discussion, TSD 2013, held in Brno, Czech Republic, in September 2014. The 70 papers provided including three invited papers have been conscientiously reviewed and chosen from 143 submissions. They specialise in issues reminiscent of corpora and language assets; speech reputation; tagging, category and parsing of textual content and speech; speech and spoken language new release; semantic processing of textual content and speech; integrating purposes of textual content and speech processing; computerized discussion platforms; in addition to multimodal ideas and modelling.

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Extra info for Text, Speech and Dialogue: 17th International Conference, TSD 2014, Brno, Czech Republic, September 8-12, 2014. Proceedings

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With this corpus of video documents, a set of video-related text documents that describes the extracted videos is added to the SuMACC corpus. Note that a description is not available for all the videos. Thus, 1,410 descriptions are collected for the 7,517 videos of the corpus. This is a real context fact: few videos have a textual document to describe their content. This set of text documents contains 9,692 unique words for a total of 56,474 running words. As expected, Table 3 shows that the videos retrieved with the thematic queries are globally considered more relevant that the ones from the initial queries.

We used a maximum entropy regression model. 60. 51. Table 6 shows the confusion matrix for the full feature set. Table 6. 75 - 15 4 1 2 5 70 15 1 1 11 To evaluate the effect of machine translation on text leveling performance, we identified the largest subset of text material by source language in the smaller set which turned out to be Farsi. We had a Farsi test set of of 60 documents. We used a phrase-based Farsi-English translation system produce the machine translation version of the documents.

3. : The Pascal recognising textual entailment challenge. , d’Alché-Buc, F. ) MLCW 2005. LNCS (LNAI), vol. 3944, pp. 177–190. Springer, Heidelberg (2006) 4. : Recognizing textual entailment: Rational, evaluation and approaches. Journal of Natural Language Engineering 15(04), i–xvii (2009) 5. : An Algorithm for generating subset of fixed size with a strong Minimal Change Property. Information Processing Letters 19, 131–133 (1984) 6. : Accurate Unlexicalized Parsing. In: Proceedings of the 41st Meeting of the Association for Computational Linguistics, pp.

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