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This publication constitutes the refereed court cases of the sixteenth foreign convention on man made Intelligence: technique, platforms, and functions, AIMSA 2014, held in Varna, Bulgaria in September 2014. The 14 revised complete papers and nine brief papers provided have been rigorously reviewed and chosen from fifty three submissions. the variety of subject matters is nearly both vast, from conventional components similar to computing device imaginative and prescient and traditional language processing to rising components reminiscent of mining the habit of Web-based communities.
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Extra resources for Artificial Intelligence: Methodology, Systems, and Applications: 16th International Conference, AIMSA 2014, Varna, Bulgaria, September 11-13, 2014. Proceedings
Boneva, and E. Tsiporkova 25. : Fast and Eﬀective Text Mining Using Linear Time Document Clustering. In: KDD 1999, pp. 16–29 (1999) 26. : Combining Local Context and WordNet Similarity for Word Sense Identiﬁcation, pp. 265–283. MIT Press, Cambridge (1998) 27. : An Information-Theoretic Deﬁnition of Similarity. In: 15th International Conference on Machine Learning, ICML, pp. 296–304 (1998) 28. : Investigating task performance of probabilistic topic models: an empirical study of plsa and lda. Information Retrieval 14(2), 178–203 (2011) 29.
C. Cercel and Ş. Trăuşan-Matu We propose a user-level method for the opinion propagation problem in discussion forum threads, taking into account the opinions written by users. For this purpose, we extract the users’ opinions from their posts in the discussion. More concretely, our solution is based on the opinions written by users on diverse themes expressed by specific noun terms that are semantically related to the subject of the discussion. Thus, our solution consists in determining whether the participants in the discussion reach complete agreement or they continue to have disparate or even contrary opinions, and therefore impossible or very hard to adapt from one to the other.
2 Methods In this section, we present our clustering method by ﬁrst reviewing the characteristics of three widely-used groups of partitioning algorithms and then by describing how experts represented by lists of keywords can be clustered. 1 Partitioning Algorithms Three partitioning algorithms are commonly used for the purpose of dividing data objects into k disjoint clusters : k-means clustering, k-medians clustering and k-medoids clustering. All three methods start by initializing a set of k cluster centers, where k is preliminarily determined.