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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Extra info for Natural Language Processing and Cognitive Science
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.