By Wojciech Cellary, Mohamed F. Mokbel, Jianmin Wang, Hua Wang, Rui Zhou, Yanchun Zhang
This quantity set LNCS 10041 and LNCS 10042 constitutes the lawsuits of the seventeenth foreign convention on net details structures Engineering, clever 2016, held in Shanghai, China, in November 2016.
The 39 complete papers and 31 brief papers awarded in those court cases have been rigorously reviewed and chosen from 233 submissions. The papers hide a variety of issues comparable to Social community facts research; Recommender platforms; subject Modeling; information variety; information Similarity; Context-Aware suggestion; Prediction; enormous info Processing; Cloud Computing; occasion Detection; info Mining; Sentiment research; score in Social Networks; Microblog information research; question Processing; Spatial and Temporal information; Graph concept; Non-Traditional Environments; and targeted consultation on information caliber and belief in colossal Data.
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Additional info for Web Information Systems Engineering – WISE 2016: 17th International Conference, Shanghai, China, November 8-10, 2016, Proceedings, Part I
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11&ZD189). References 1. : Text normalization and semantic indexing to enhance instant messaging and sms spam filtering. Knowl. Based Syst. 108, 25–32 (2016) 2. : A phrase-based statistical model for sms text normalization. In: Proceedings of the Joint Conference on Annual Meeting of the Association for Computational Linguistics and International Conference on Computational Linguistics, pp. 33–40 (2006) 3. : Event discovery in social media feeds. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics, pp.
The result is shown in Fig. 2. It is obvious in Fig. e. the distance d equals to 0) except for DataSet 6 and this is because that on DataSet 6, most of the seeds nodes selected are closely connected but cannot influence each other since the influence probabilities on the edges are relatively small, and (2) the best covered distances for every dataset are different because of the different topologies and the different influence probabilities on the edges, and (3) in Fig. 2(d) and (e), the influence spreads with different covered distances Attribute-Based Influence Maximization in Social Networks 13 go up alternately, which is reasonable since the propagation probability for each edge in the network is different and the best covered distance for each node is also different, and (4) when d = 3, in most cases the performance of SoPCA is relatively the worst since the influence scope of a node is limited and if d is too large, many influential nodes will be covered by accident which will influence the quality of seed nodes.
Results show that our system achieves the best performance (a 10 % absolute increment compared to state-of-the-art). The proposed approach can be deployed as a preprocessing step for various NLP application to handle twitter message. 2 Related Work With the rapid development of social media, text normalization system has drawn increasing attention in recent years. There are many systems proposed for tackling text normalization. Previous methods are mainly divided into two Twitter Normalization via 1-to-N Recovering 21 directions: NC-based model (Noisy Channel) and SMT-based model (Statistical Machine Translation).
