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.
Read or Download Computational Linguistics and Intelligent Text Processing: 15th International Conference, CICLing 2014, Kathmandu, Nepal, April 6-12, 2014, Proceedings, Part I PDF
Similar data mining books
This ebook constitutes the refereed lawsuits of the eleventh foreign Workshop on Computational Processing of the Portuguese Language, PROPOR 2014, held in Sao Carlos, Brazil, in October 2014. The 14 complete papers and 19 brief papers provided during this quantity have been conscientiously reviewed and chosen from sixty three submissions.
This e-book investigates the layout and implementation of marketplace mechanisms to discover how they could aid wisdom- and innovation administration inside of organisations. The e-book makes use of a multi-method layout, combining qualitative and quantitative situations with experimentation. First the booklet studies conventional techniques to fixing the matter in addition to markets as a key mechanism for challenge fixing.
This e-book provides case reports in statistical computing for facts research. every one case examine addresses a statistical program with a spotlight on evaluating diversified computational methods and explaining the reasoning in the back of them. The case reports can function fabric for teachers educating classes in statistical computing and utilized facts.
Concentrating on up to date man made intelligence types to resolve development power difficulties, man made Intelligence for development strength research experiences lately constructed versions for fixing those concerns, together with distinctive and simplified engineering tools, statistical equipment, and synthetic intelligence equipment.
- Support Vector Machines
- Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner
- Introduction to Machine Learning (3rd Edition) (Adaptive Computation and Machine Learning)
- Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage
- Relational Data Clustering: Models, Algorithms, and Applications
- Big Data Technology and Applications: First National Conference, BDTA 2015, Harbin, China, December 25-26, 2015. Proceedings
Extra resources for Computational Linguistics and Intelligent Text Processing: 15th International Conference, CICLing 2014, Kathmandu, Nepal, April 6-12, 2014, Proceedings, Part I
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.  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  “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”.