Search (13 results, page 1 of 1)

Li, J.; Sun, A.; Xing, Z.: To do or not to do : distill crowdsourced negative caveats to augment api documentation (2018) 0.02
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Abstract

Negative caveats of application programming interfaces (APIs) are about "how not to use an API," which are often absent from the official API documentation. When these caveats are overlooked, programming errors may emerge from misusing APIs, leading to heavy discussions on Q&A websites like Stack Overflow. If the overlooked caveats could be mined from these discussions, they would be beneficial for programmers to avoid misuse of APIs. However, it is challenging because the discussions are informal, redundant, and diverse. For this, for example, we propose Disca, a novel approach for automatically Distilling desirable API negative caveats from unstructured Q&A discussions. Through sentence selection and prominent term clustering, Disca ensures that distilled caveats are context-independent, prominent, semantically diverse, and nonredundant. Quantitative evaluation in our experiments shows that the proposed Disca significantly outperforms four text-summarization techniques. We also show that the distilled API negative caveats could greatly augment API documentation through qualitative analysis.
Zheng, R.; Li, J.; Chen, H.; Huang, Z.: ¬A framework for authorship identification of online messages : writing-style features and classification techniques (2006) 0.01
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Abstract

With the rapid proliferation of Internet technologies and applications, misuse of online messages for inappropriate or illegal purposes has become a major concern for society. The anonymous nature of online-message distribution makes identity tracing a critical problem. We developed a framework for authorship identification of online messages to address the identity-tracing problem. In this framework, four types of writing-style features (lexical, syntactic, structural, and content-specific features) are extracted and inductive learning algorithms are used to build feature-based classification models to identify authorship of online messages. To examine this framework, we conducted experiments on English and Chinese online-newsgroup messages. We compared the discriminating power of the four types of features and of three classification techniques: decision trees, backpropagation neural networks, and support vector machines. The experimental results showed that the proposed approach was able to identify authors of online messages with satisfactory accuracy of 70 to 95%. All four types of message features contributed to discriminating authors of online messages. Support vector machines outperformed the other two classification techniques in our experiments. The high performance we achieved for both the English and Chinese datasets showed the potential of this approach in a multiple-language context.

Date

22. 7.2006 16:14:37
Min, C.; Ding, Y.; Li, J.; Bu, Y.; Pei, L.; Sun, J.: Innovation or imitation : the diffusion of citations (2018) 0.01
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Abstract

Citations in scientific literature are important both for tracking the historical development of scientific ideas and for forecasting research trends. However, the diffusion mechanisms underlying the citation process remain poorly understood, despite the frequent and longstanding use of citation counts for assessment purposes within the scientific community. Here, we extend the study of citation dynamics to a more general diffusion process to understand how citation growth associates with different diffusion patterns. Using a classic diffusion model, we quantify and illustrate specific diffusion mechanisms which have been proven to exert a significant impact on the growth and decay of citation counts. Experiments reveal a positive relation between the "low p and low q" pattern and high scientific impact. A sharp citation peak produced by rapid change of citation counts, however, has a negative effect on future impact. In addition, we have suggested a simple indicator, saturation level, to roughly estimate an individual article's current stage in the life cycle and its potential to attract future attention. The proposed approach can also be extended to higher levels of aggregation (e.g., individual scientists, journals, institutions), providing further insights into the practice of scientific evaluation.

Zhu, Q.; Kong, X.; Hong, S.; Li, J.; He, Z.: Global ontology research progress : a bibliometric analysis (2015) 0.01

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Date: 20. 1.2015 18:30:22
17. 9.2018 18:22:23

Lin, X.; Li, J.; Zhou, X.: Theme creation for digital collections (2008) 0.01

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Source: Metadata for semantic and social applications : proceedings of the International Conference on Dublin Core and Metadata Applications, Berlin, 22 - 26 September 2008, DC 2008: Berlin, Germany / ed. by Jane Greenberg and Wolfgang Klas

Li, J.; Shi, D.: Sleeping beauties in genius work : when were they awakened? (2016) 0.00

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Date: 22. 1.2016 14:13:32

Li, J.; Zhang, Z.; Li, X.; Chen, H.: Kernel-based learning for biomedical relation extraction (2008) 0.00
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Abstract

Relation extraction is the process of scanning text for relationships between named entities. Recently, significant studies have focused on automatically extracting relations from biomedical corpora. Most existing biomedical relation extractors require manual creation of biomedical lexicons or parsing templates based on domain knowledge. In this study, we propose to use kernel-based learning methods to automatically extract biomedical relations from literature text. We develop a framework of kernel-based learning for biomedical relation extraction. In particular, we modified the standard tree kernel function by incorporating a trace kernel to capture richer contextual information. In our experiments on a biomedical corpus, we compare different kernel functions for biomedical relation detection and classification. The experimental results show that a tree kernel outperforms word and sequence kernels for relation detection, our trace-tree kernel outperforms the standard tree kernel, and a composite kernel outperforms individual kernels for relation extraction.
Zhang, C.; Zeng, D.; Li, J.; Wang, F.-Y.; Zuo, W.: Sentiment analysis of Chinese documents : from sentence to document level (2009) 0.00
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Abstract

User-generated content on the Web has become an extremely valuable source for mining and analyzing user opinions on any topic. Recent years have seen an increasing body of work investigating methods to recognize favorable and unfavorable sentiments toward specific subjects from online text. However, most of these efforts focus on English and there have been very few studies on sentiment analysis of Chinese content. This paper aims to address the unique challenges posed by Chinese sentiment analysis. We propose a rule-based approach including two phases: (1) determining each sentence's sentiment based on word dependency, and (2) aggregating sentences to predict the document sentiment. We report the results of an experimental study comparing our approach with three machine learning-based approaches using two sets of Chinese articles. These results illustrate the effectiveness of our proposed method and its advantages against learning-based approaches.
Li, J.; Willett, P.: ArticleRank : a PageRank-based alternative to numbers of citations for analysing citation networks (2009) 0.00
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Abstract

Purpose - The purpose of this paper is to suggest an alternative to the widely used Times Cited criterion for analysing citation networks. The approach involves taking account of the natures of the papers that cite a given paper, so as to differentiate between papers that attract the same number of citations. Design/methodology/approach - ArticleRank is an algorithm that has been derived from Google's PageRank algorithm to measure the influence of journal articles. ArticleRank is applied to two datasets - a citation network based on an early paper on webometrics, and a self-citation network based on the 19 most cited papers in the Journal of Documentation - using citation data taken from the Web of Knowledge database. Findings - ArticleRank values provide a different ranking of a set of papers from that provided by the corresponding Times Cited values, and overcomes the inability of the latter to differentiate between papers with the same numbers of citations. The difference in rankings between Times Cited and ArticleRank is greatest for the most heavily cited articles in a dataset. Originality/value - This is a novel application of the PageRank algorithm.
Zhao, S.X.; Zhang, P.L.; Li, J.; Tan, A.M.; Ye, F.Y.: Abstracting the core subnet of weighted networks based on link strengths (2014) 0.00
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Abstract

Most measures of networks are based on the nodes, although links are also elementary units in networks and represent interesting social or physical connections. In this work we suggest an option for exploring networks, called the h-strength, with explicit focus on links and their strengths. The h-strength and its extensions can naturally simplify a complex network to a small and concise subnetwork (h-subnet) but retains the most important links with its core structure. Its applications in 2 typical information networks, the paper cocitation network of a topic (the h-index) and 5 scientific collaboration networks in the field of "water resources," suggest that h-strength and its extensions could be a useful choice for abstracting, simplifying, and visualizing a complex network. Moreover, we observe that the 2 informetric models, the Glänzel-Schubert model and the Hirsch model, roughly hold in the context of the h-strength for the collaboration networks.
Du, Q.; Li, J.; Du, Y.; Wang, G.A.; Fan, W.: Predicting crowdfunding project success based on backers' language preferences (2021) 0.00
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Abstract

Project success is critical in the crowdfunding domain. Rather than the existing project-centric prediction methods, we propose a novel backer-centric prediction method. We identify each backer's preferences based on their pledge history and calculate the cosine similarity between backer's preferences and the project as each backer's persuasibility. Finally, we aggregate all the backers' persuasibility to predict project success. To validate our method, we crawled data on 183,886 projects launched during or before December 2014 on Kickstarter, a crowdfunding website. We selected 4,922 backers with a total of 442,793 pledges to identify backers' preferences. The results show that a backer is more likely to be persuaded by a project that is more similar to the backer's preferences. Our findings not only demonstrate the efficacy of backers' pledge history for predicting crowdfunding project success but also verify that a backer-centric method can supplement the existing project-centric approaches. Our model and findings enable crowdfunding platform agencies, fund-seeking entrepreneurs, and investors to predict the success of a crowdfunding project.
Li, J.; Zhang, P.; Cao, J.: External concept support for group support systems through Web mining (2009) 0.00
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Abstract

External information plays an important role in group decision-making processes, yet research about external information support for Group Support Systems (GSS) has been lacking. In this study, we propose an approach to build a concept space to provide external concept support for GSS users. Built on a Web mining algorithm, the approach can mine a concept space from the Web and retrieve related concepts from the concept space based on users' comments in a real-time manner. We conduct two experiments to evaluate the quality of the proposed approach and the effectiveness of the external concept support provided by this approach. The experiment results indicate that the concept space mined from the Web contained qualified concepts to stimulate divergent thinking. The results also demonstrate that external concept support in GSS greatly enhanced group productivity for idea generation tasks.
Shi, D.; Rousseau, R.; Yang, L.; Li, J.: ¬A journal's impact factor is influenced by changes in publication delays of citing journals (2017) 0.00
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Abstract

In this article we describe another problem with journal impact factors by showing that one journal's impact factor is dependent on other journals' publication delays. The proposed theoretical model predicts a monotonically decreasing function of the impact factor as a function of publication delay, on condition that the citation curve of the journal is monotone increasing during the publication window used in the calculation of the journal impact factor; otherwise, this function has a reversed U shape. Our findings based on simulations are verified by examining three journals in the information sciences: the Journal of Informetrics, Scientometrics, and the Journal of the Association for Information Science and Technology.

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