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 resembling corpora and language assets; speech popularity; tagging, class 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 structures; in addition to multimodal ideas and modelling.
Read or Download Text, Speech and Dialogue: 17th International Conference, TSD 2014, Brno, Czech Republic, September 8-12, 2014. Proceedings PDF
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Additional info for Text, Speech and Dialogue: 17th International Conference, TSD 2014, Brno, Czech Republic, September 8-12, 2014. Proceedings
The set of n words is considered as the first expanded query. This first extension step is followed by a second one, where we use the first expanded query to select the k-best concepts. Finally, a set of m words are extracted by cumulating conditional word probabilities belonging to the m best LDA classes. This step yields to get the m most relevant words for each initial concept. This second expanded query is submitted to the Syllabs multimedia fetcher to collect videos matching the final query.
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.
Roukos, J. Quin, and T. Ward equal. Another aspect of our data is a skewed distribution of the levels with a severe under-representation of the 0+/1 level at 2% of the documents with the other categories at 23%, 58%, and 17%, respectively. We present, in Section 2, our work on ILR level annotation, in Section 3, the data set, in Section 4, our text leveling results, and in Section 5, our conclusions. 2 Text Leveling Annotation We attempted to train a small pool of 5 annotators to perform the ILR text leveling of English documents.