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 awarded used to be conscientiously 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 solution and observe experience disambiguation; semantics and discussion; computer translation and multilingualism; sentiment research and emotion detection; opinion mining and social community research; normal language new release and textual content summarization; details retrieval, query answering, and knowledge extraction; textual content class; speech processing; and applications.

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Example text

113] employed Maximum-Entropy to train a switch variable based on POS tags of words and use it to separate aspect and sentiment words. [62] added user feedback into LDA as a response variable connected to each document. In [60], a semi-supervised model was proposed. DF-LDA [2] also represents a semi-supervised model, which allows the user to set must-link and cannotlink constraints. A must-link means that two terms must be in the same topic, while a cannot-link means that two terms cannot be in the same topic.

The Journal of Machine Learning Research 3, 993–1022 (2003) 8. : Glue for the mental world. Nature 421, 212–213 (2003) 9. : Opinion summarisation through sentence extraction: An investigation with movie reviews. In: Proceedings of the 35th International ACM SIGIR Conference on Research and Development in Information Retrieval SIGIR 2012, pp. 1121–1122 (2012) 10. : Learning document-level semantic properties from free-text annotations. Journal of Artificial Intelligence Research 34(2), 569 (2009) 11.

In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pp. 1069–1078. ACM (2014) 106. : Latent aspect rating analysis on review text data: a rating regression approach. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 783–792. ACM (2010) 107. : Huaqing Min. Product aspect extraction supervised with online domain knowledge. Knowledge-Based Systems 71, 86–100 (2014) 108. : Creating subjective and objective sentence classifiers from unannotated texts.

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