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By Alexander Gelbukh

This two-volume set, such as LNCS 8403 and LNCS 8404, constitutes the completely refereed court cases of the 14th foreign convention on clever textual content Processing and Computational Linguistics, CICLing 2014, held in Kathmandu, Nepal, in April 2014. The eighty five revised papers awarded including four invited papers have been rigorously reviewed and chosen from three hundred submissions. The papers are equipped within the following topical sections: lexical assets; record illustration; morphology, POS-tagging, and named entity acceptance; syntax and parsing; anaphora solution; spotting textual entailment; semantics and discourse; typical language iteration; sentiment research and emotion acceptance; opinion mining and social networks; computing device translation and multilingualism; details retrieval; textual content category and clustering; textual content summarization; plagiarism detection; sort and spelling checking; speech processing; and applications.

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Extra resources for Computational Linguistics and Intelligent Text Processing: 15th International Conference, CICLing 2014, Kathmandu, Nepal, April 6-12, 2014, Proceedings, Part I

Example text

This can be done by refining the set of constraints (CON). 1 Refining CON The issues we have reported in the previous section do not mean that automatic methods are flawed, but they have a number of drawbacks that should be addressed. The acquisition process, based on an analysis of co-occurrences of the verb with its immediate complements (along with filtering techniques) makes the approach highly functional. It is a good approximation of the problem. However, this model does not take into account external constraints.

5 F-measure) on high frequency verbs with the same combination of features (SCFs and selectional preferences) and the same clustering method (spectral clustering) as for English. Falk et al. [26] employed a neural clustering method for French verbs. They achieved 70 F-measure when evaluating on a slightly modified version of the Sun et al. 2010 gold standard for French. However, the method is not fully comparable to other works mentioned here because it uses features from lexical resources rather than those obtained solely by NLP.

Most authors agree on the fact that complements should be divided between arguments and adjuncts but the distinction between these two categories is far from obvious. Some linguistic tests exist (can the complement be deleted without changing the meaning of the sentence? Can it be moved easily? Can it be pronominalized? ) but none of these tests is sufficient or discriminatory enough. As outlined by Manning [18] “rather than maintaining a categorical argument / adjunct distinction and having to make in/out decisions about such cases, we might instead try to represent SCF information as a probability distribution over argument frames, with different verbal dependents expected to occur with a verb with a certain probability”.

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