Web Information Systems Engineering – WISE 2013: 14th by Nguyen Quoc Viet Hung, Nguyen Thanh Tam, Lam Ngoc Tran, Karl

By Nguyen Quoc Viet Hung, Nguyen Thanh Tam, Lam Ngoc Tran, Karl Aberer (auth.), Xuemin Lin, Yannis Manolopoulos, Divesh Srivastava, Guangyan Huang (eds.)

This ebook constitutes the complaints of the 14th overseas convention on internet info platforms Engineering, clever 2013, held in Nanjing, China, in October 2013. The forty eight complete papers, 29 brief papers, and 10 demo and five problem papers, awarded within the two-volume court cases LNCS 8180 and 8181, have been rigorously reviewed and chosen from 198 submissions. they're geared up in topical sections named: internet mining; net advice; net providers; facts engineering and database; semi-structured information and modeling; net facts integration and hidden internet; problem; social internet; info extraction and multilingual administration; networks, graphs and Web-based enterprise approaches; occasion processing, internet tracking and administration; and cutting edge innovations and creations.

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Extra resources for Web Information Systems Engineering – WISE 2013: 14th International Conference, Nanjing, China, October 13-15, 2013, Proceedings, Part II

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To model these types of workers, we use two parameters: sensitivity—the proportion of actual positives that are correctly identified—and specificity—the proportion of negatives that are correctly identified. Following the statistical result in [12], we set randomly the sensitivity and specificity of each type of workers as follows. H. Nguyen et al. 9, 1]. 9]. 4] is selected. 6. 8, 1]. Answer Simulator. This component generates worker answers for two types of questions. (1) Binary-choice (yes/no): in the literature, the two-coin model [22] is used to generate worker answers for each object.

Therefore we develop some features aiming to capture the linguistic properties of a tweet which include the percentage of tokens that are open-class, the percentage of tokens that are nouns, the percentage of tokens that are verbs and the percentage of tokens that are adjectives or adverbs. We use the Twitter Part-of-Speech Tagging 8 to tag the tweets [3]. Fluency: The fluency of a text can capture the readability of a tweet and we use language model to tackle the fluency of text. We take the probability of a tweet t under a particular language model (LM) as the fluency score F (t).

In general, GLAD as well as EM-based approaches are sensitive to arbitrary initializations. Particularly, GLAD’s performance depends on the initial value of worker expertise α and question difficulty β. In fact, there is no theoretical analysis for the performance guarantees and it is necessary to have a benchmark for evaluating different techniques in the same setting. Iterative Learning. Iterative Learning (ITER) is an iterative technique based on standard belief propagation [10]. It also estimates the question difficulty and the worker expertise, but slightly different in details.

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