Computational Linguistics and Intelligent Text Processing: by Alexander Gelbukh

By Alexander Gelbukh

The volumes LNCS 9041 and 9042 represent the complaints 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 geared up in topical sections on grammar formalisms and lexical assets; morphology and chunking; syntax and parsing; anaphora answer and note experience disambiguation; semantics and discussion; computer translation and multilingualism; sentiment research and emotion detection; opinion mining and social community research; average language iteration and textual content summarization; details retrieval, query answering, and data extraction; textual content type; speech processing; and applications.

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Extra info for Computational Linguistics and Intelligent Text Processing: 16th International Conference, CICLing 2015, Cairo, Egypt, April 14-20, 2015, Proceedings, Part II

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Although the dataset is publicly available, it is limited in size and only covers the movie reviews domain. Abdul-Mageed & Diab [1] presented the AWATIF multi-genre corpus of Modern Standard Arabic labeled for subjectivity and Sentiment Analysis. The corpus was built from different resources including the Penn Arabic Treebank, Wikipedia Talk Pages and Web forums. It was manually annotated by trained annotators and through crowd sourcing. The dataset targets only Modern Standard Arabic (MSA) which is not commonly used when writing reviews on most websites and social media.

In the proposed approach, performed by means of latent semantic analysis, these documents were built up from common emotional expressions in social streams. Bell et al. [5] proposed a novel approach to social data analysis, exploring the use of microblogging to manage interaction between humans and robots, and evaluating an architecture that extends the use of social networks to connect humans and devices. The approach used NLP techniques to extract features of interest from textual data retrieved from a microblogging platform in real-time and, hence, to generate appropriate executable code for the robot.

In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1116–1125. ACM (2014) 18 E. Cambria et al. 24. : SeNTU: Sentiment analysis of tweets by combining a rule-based classifier with supervised learning. In: Proceedings of the International Workshop on Semantic Evaluation (SemEval 2015) (2015) 25. : Joins that generalize: Text classification using whirl. In: KDD, pp. 169–173 (1998) 26. : Automatic processing of large corpora for the resolution of anaphora references.

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