By Alexander Gelbukh
The volumes LNCS 9041 and 9042 represent the complaints of the sixteenth overseas convention on Computational Linguistics and clever textual content Processing, CICLing 2015, held in Cairo, Egypt, in April 2015.
The overall of ninety five complete papers offered was once conscientiously reviewed and chosen from 329 submissions. They have been prepared in topical sections on grammar formalisms and lexical assets; morphology and chunking; syntax and parsing; anaphora solution and be aware feel disambiguation; semantics and discussion; desktop translation and multilingualism; sentiment research and emotion detection; opinion mining and social community research; ordinary language new release and textual content summarization; info retrieval, query answering, and data extraction; textual content category; speech processing; and purposes.
Read or Download Computational Linguistics and Intelligent Text Processing: 16th International Conference, CICLing 2015, Cairo, Egypt, April 14-20, 2015, Proceedings, Part I (Lecture Notes in Computer Science) PDF
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Extra resources for Computational Linguistics and Intelligent Text Processing: 16th International Conference, CICLing 2015, Cairo, Egypt, April 14-20, 2015, Proceedings, Part I (Lecture Notes in Computer Science)
Odstraňování těchto bariér může být podle ministra Karla Dyby někdy významnější pomocí podnikání než finanční podpora státu. 5 The generalization of the Actor and of other valency members (participants and some adjuncts) belongs to frequent phenomena in the PDT. ACT), there is a special form in Czech (called deagentization, or, in older tradition, reflexive passive), see Fig. 5 and Fig. 1. General ACT often occurs in passive sentences (see Fig. 6). ); #Oblfm is used as the lemma for a generalized adjunct.
It is clear that the need for cross-linguistic consistency and perspicuity often runs counter to the requirements of optimal parsability for speciﬁc languages. We therefore envisage that parsers expected to output UD representations often will have to use diﬀerent representations internally. In fact, we believe that research on ﬁnding optimal representations for parsers, which has been a dominant theme in constituency-based parsing for the last twenty years, is an underexploited area in dependency parsing.
I also want to thank Masayuki Asahara, Cristina Bosco, Binyam Ephrem, Rich´ard Farkas, Jennifer Foster, Koldo Gojenola, Hiroshi Kanayama, Jenna Kanerva, Veronika Laippala, Alessandro Lenci, Teresa Lynn, Anna Missil¨a, Verginica Mititelu, Yusuke Miyao, Simonetta Montemagni, Shinsuke Mori, Petya Osenova, Prokopis Prokopidis, Mojgan Seraji, Maria Simi, Kiril Simov, Aaron Smith, Takaaki Tanaka, Sumire Uematsu, and Veronika Vincze, for contributions to language-speciﬁc guidelines and/or treebanks. The views expressed in this paper on the status and motivation of UD are primarily my own and are not necessarily shared by all contributors to the project.