Download Natural Language Processing and Cognitive Science by Bernadette Sharp, Rodolfo Delmonte PDF

By Bernadette Sharp, Rodolfo Delmonte

This publication contains peer reviewed articles from the typical Language Processing and Cognitive technological know-how (NLPCS) 2014 assembly in October 2014 workshop. The assembly fosters interactions between researchers and practitioners in NLP via taking a Cognitive technology standpoint. Articles conceal subject matters comparable to synthetic intelligence, computational linguistics, psycholinguistics, cognitive psychology and language studying.

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We have adopted Jiang & Conrath’s similarity measure for our final experiment because in our various experiments with each of the semantic similarity measures, we found it to be the best performing similarity measure. 6 Jiang & Conrath’s Similarity Measure Jiang & Conrath’s similarity measure (Jaing & Conrath, 1998) is a similarity measure derived from corpus statistics and WordNet lexical taxonomy. It is a combined model that is derived from the edge-based notion by adding the information content as a decision factor.

1992) worked on a variation called simulated Annealing. In this work, they define a function E that reflects the combination of word senses in a given text whose minimum should correspond to the correct choice of word senses. For a given combination of senses, all corresponding definitions from a dictionary are collected, and each word appearing at least once in these definitions receives a score equal to its number of occurrences. Adding all these scores together gives the redundancy of the text.

And Seneff, S. (2008). Correcting misuse of verb forms. In: Proceedings of ACL-08: HLT, Columbus, Ohio, pp. 174–182. , Nenkova, A. (2011). c sentences by leveraging discourse annotations. In: Proceedings of the 5th International Joint Conference on Natural Language Processing, Chiang Mai, Thailand, November 8–13, 2011, pp. 605–613. , Nenkova, A. (2013). What Makes Writing Great? First Experiments on Article Quality Prediction in the Science Journalism Domain. Transactions of the Association for Computational Linguistics, 1 , 2013, 341–352.

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