By Lukas Michelbacher, Qi Han, Hinrich Schütze (auth.), Alexander Gelbukh (eds.)
This two-volume set, such as LNCS 7816 and LNCS 7817, constitutes the completely refereed lawsuits of the thirteenth foreign convention on desktop Linguistics and clever Processing, CICLING 2013, hung on Samos, Greece, in March 2013. the entire of ninety one contributions offered used to be conscientiously reviewed and chosen for inclusion within the court cases. The papers are prepared in topical sections named: basic ideas; lexical assets; morphology and tokenization; syntax and named entity attractiveness; be aware feel disambiguation and coreference solution; semantics and discourse; sentiment, polarity, subjectivity, and opinion; desktop translation and multilingualism; textual content mining, details extraction, and knowledge retrieval; textual content summarization; stylometry and textual content simplification; and applications.
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Extra info for Computational Linguistics and Intelligent Text Processing: 14th International Conference, CICLing 2013, Samos, Greece, March 24-30, 2013, Proceedings, Part I
We analyzed several properties of syntactic n-grams and presented an example of a phrase with extracted sn-grams. A shortcoming of sn-grams is that syntactic parsing is necessary prior to their construction. We tried the concept of sn-grams in the task of authorship attribution and obtained very promising results for a corpus of three and seven authors. In our experiments, the best results were always achieved by SVM classifier, as compared with NB and J48. We would like to mention the following directions of future work: − − − − − − − − − − Experiments with all feature sets on larger corpus (7 authors, or more).
Interim balloon altitude... a British balloon near B´ e... wind hot-air balloon gas is-a balloonen , Ballonde , aerostatoes , globusca , pallone aerostaticoit , ballonfr , montgolﬁ`erefr high wind blow gas is-a WordNet Fig. 2. , balloon1n has-part gasbag1n ). in which part of the Linked Open Data cloud is made up of interlinked linguistic resources , aimed at fostering integration, interoperability and reuse on the Semantic Web . 1 and brieﬂy illustrate its features. Then, in Section 3 we provide statistics, in Section 4 we take a tour of the new Web interface and in Section 5 we show how to get started with the BabelNet API.
This is particularly interesting in the cross-topic setting Den → Dph , where we apply the model learned on a more general domain (web corpus) to a specialized domain (physics) without a performance drop. This is good news because it is evidence that we can use feature adaptation instead of expensive labeling with trained domain experts in certain cases. We observe that the reverse is not the case. On Den , the in-domain system remains unrivaled and the models trained on Dph and Dde are unable to exploit feature adaptation to the same extent.