By Alexander Gelbukh
The volumes LNCS 9041 and 9042 represent the court cases of the sixteenth foreign 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 provided used to be rigorously 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 answer and note experience disambiguation; semantics and discussion; desktop translation and multilingualism; sentiment research and emotion detection; opinion mining and social community research; ordinary language iteration and textual content summarization; info retrieval, query answering, and knowledge extraction; textual content class; speech processing; and applications.
Read or Download Computational Linguistics and Intelligent Text Processing: 16th International Conference, CICLing 2015, Cairo, Egypt, April 14-20, 2015, Proceedings, Part II PDF
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Web mining, Internet security, cyber-issue detection, and social media marketing, need microtext feature selection and classification. Fig. 1. The CLSA model 6 E. Cambria et al. Existing sentiment resources developed on non-microblogging data, in fact, turn out to be very inaccurate on informal text . Some of the fundamental characteristics of microtext are a highly relaxed spelling and the reliance on abbreviations, acronyms, and emoticons . , information extraction, automated summarization, and text-to-speech, which have been developed for conventional English text.
Psychologist Gregory Murphy began his highly acclaimed book  with the statement “Concepts are the glue that holds our mental world together”. Still, Nature magazine book review calls it an understatement because “Without concepts, there would be no mental world in the first place”. Undoubtedly, the ability to conceptualize is a defining characteristic of humanity. We focus on conceptualizing from texts or words. For example, given the word “India,” a person will form in his mind concept such as a country or region.
Using emoticons to reduce dependency in machine learning techniques for sentiment classification. In: Proceedings of the ACL Student Research Workshop, pp. 43–48. Association for Computational Linguistics (2005) 90. : Politwi: Early detection of emerging political topics on twitter and the impact on concept-level sentiment analysis. Knowledge-Based Systems 69, 14–23 (2014) 91. : Learning extraction patterns for subjective expressions. In: Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing, pp.