Lei Wang

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Lei Wang is an author.


Only those publications related to wikis are shown here.
Title Keyword(s) Published in Language DateThis property is a special property in this wiki. Abstract R C
A piece of my mind: A sentiment analysis approach for online dispute detection 52nd Annual Meeting of the Association for Computational Linguistics, ACL 2014 - Proceedings of the Conference English 2014 We investigate the novel task of online dispute detection and propose a sentiment analysis solution to the problem: we aim to identify the sequence of sentence-level sentiments expressed during a discussion and to use them as features in a classifier that predicts the DISPUTE/NON-DISPUTE label for the discussion as a whole. We evaluate dispute detection approaches on a newly created corpus of Wikipedia Talk page disputes and find that classifiers that rely on our sentiment tagging features outperform those that do not. The best model achieves a very promising F1 score of 0.78 and an accuracy of 0.80. 0 0
Encoding document semantic into binary codes space Lecture Notes in Computer Science English 2014 We develop a deep neural network model to encode document semantic into compact binary codes with the elegant property that semantically similar documents have similar embedding codes. The deep learning model is constructed with three stacked auto-encoders. The input of the lowest auto-encoder is the representation of word-count vector of a document, while the learned hidden features of the deepest auto-encoder are thresholded to be binary codes to represent the document semantic. Retrieving similar document is very efficient by simply returning the documents whose codes have small Hamming distances to that of the query document. We illustrate the effectiveness of our model on two public real datasets - 20NewsGroup and Wikipedia, and the experiments demonstrate that the compact binary codes sufficiently embed the semantic of documents and bring improvement in retrieval accuracy. 0 0
Exploring simultaneous keyword and key sentence extraction: Improve graph-based ranking using Wikipedia Graph
Markov chain
ACM International Conference Proceeding Series English 2012 Summarization and Keyword Selection are two important tasks in NLP community. Although both aim to summarize the source articles, they are usually treated separately by using sentences or words. In this paper, we propose a two-level graph based ranking algorithm to generate summarization and extract keywords at the same time. Previous works have reached a consensus that important sentence is composed by important keywords. In this paper, we further study the mutual impact between them through context analysis. We use Wikipedia to build a two-level concept-based graph, instead of traditional term-based graph, to express their homogenous relationship and heterogeneous relationship. We run PageRank and HITS rank on the graph to adjust both homogenous and heterogeneous relationships. A more reasonable relatedness value will be got for key sentence selection and keyword selection. We evaluate our algorithm on TAC 2011 data set. Traditional term-based approach achieves a score of 0.255 in ROUGE-1 and a score of 0.037 and ROUGE-2 and our approach can improve them to 0.323 and 0.048 separately. 0 0
Detecting community kernels in large social networks Auxiliary communities
Community kernel detection
Community kernels
Social network
Proceedings - IEEE International Conference on Data Mining, ICDM English 2011 In many social networks, there exist two types of users that exhibit different influence and different behavior. For instance, statistics have shown that less than 1% of the Twitter users (e.g. entertainers, politicians, writers) produce 50% of its content [1], while the others (e.g. fans, followers, readers) have much less influence and completely different social behavior. In this paper, we define and explore a novel problem called community kernel detection in order to uncover the hidden community structure in large social networks. We discover that influential users pay closer attention to those who are more similar to them, which leads to a natural partition into different community kernels. We propose GREEDY and WEBA, two efficient algorithms for finding community kernels in large social networks. GREEDY is based on maximum cardinality search, while WEBA formalizes the problem in an optimization framework. We conduct experiments on three large social networks: Twitter, Wikipedia, and Coauthor, which show that WEBA achieves an average 15%- 50% performance improvement over the other state-of-the-art algorithms, and WEBA is on average 6-2,000 times faster in detecting community kernels. 0 0
A method of building Chinese field association knowledge from Wikipedia Chinese documents
Feature fields
Field association terms
Field recognition
2009 International Conference on Natural Language Processing and Knowledge Engineering, NLP-KE 2009 English 2009 Field Association (FA) terms form a limited set of discriminating terms that give us the knowledge to identify document fields. The primary goal of this research is to make a system that can imitate the process whereby humans recognize the fields by looking at a few Chinese FA terms in a document. This paper proposes a new approach to build a Chinese FA terms dictionary automatically from Wikipedia. 104,532 FA terms are added in the dictionary. The resulting FA terms by using this dictionary are applied to recognize the fields of 5,841 documents. The average accuracy in the experiment is 92.04%. The results show that the presented method is effective in building FA terms from Wikipedia automatically. 0 0
Rich Collaboration on the Move: Talk to Your Mobile Audio Wiki Speech
NGMAST English 2009 0 0
Rich collaboration on the move: Talk to your mobile audio wiki Mobile
Speech collaboration
NGMAST 2009 - 3rd International Conference on Next Generation Mobile Applications, Services and Technologies English 2009 Wikis are proven to be simple yet efficient tools for asynchronous collaboration where multiple users can collaboratively edit shared web documents. While desktop users are comfortable with text-based wikis, the latest developments of 3G networks and smart phones begs the question what is the appropriate model for wiki collaboration on mobile devices which can accommodate mobile user behavior? The properties of mobile collaborative work practices and mobile computing technical constraints require a different model for interacting with wikis. In this paper, we introduced a new wiki model which utilizes speech, rather than text, as the medium for collaboration. Thus users listen or talk to contribute to a shared, marked up audio clip, with their existing mobile devices. An evaluation of system usability and user acceptance of such speech-based wiki-style collaboration has been conducted. The evaluation indentified that our approach does indeed help mobile users to effectively participate in collaborative tasks. 0 0
An audio wiki supporting mobile collaboration SAC English 2008 Wikis have proved to be very effective collaboration and knowledge management tools in large variety of fields thanks to their simplicity and flexible nature. Another important development for the internet is the emergence of powerful mobile devices supported by fast and reliable wireless networks. The combination of these developments begs the question of how to extend wikis on mobile devices and how to leverage mobile devices' rich modalities to supplement current wikis. Realizing that composing and consuming through auditory channel is the most natural and efficient way for mobile device user, this paper explores the use of audio as the medium of wiki. Our work, as the first step towards this direction, creates a framework called Mobile Audio Wiki which facilitates asynchronous audio-mediated collaboration on the move. In this paper, we present the design of Mobile Audio Wiki. As a part of such design, we propose an innovative approach for a light-weight audio content annotation system for enabling group editing, versioning and cross-linking among audio clips. To elucidate the novel collaboration model introduced by Mobile Audio Wiki, its four usage modes are identified and presented in storyboard format. Finally, we describe the initial design for presentation and navigation of Mobile Audio Wiki. 0 0
Lesson-preparing innovation: A new effective approach on implementing collaborative lesson-preparing activities within eduwiki Architecture
Collaborative lesson-preparing
Proceedings - International Conference on Computer Science and Software Engineering, CSSE 2008 English 2008 Lesson-preparing is an important stage in the teaching activity processes. This paper argues that collaborative lesson-preparing within Eduwiki environment is a new effective approach for teachers' collaboration and teaching. Further, the architecture of Eduwiki is effective in monitoring and recording processes of lesson-preparing. The paper also provides a case to show the mechanism of collaborative lesson-preparing activity. Teachers' evaluations have proved that Eduwiki is effective in motivating peer-supported collaborative lesson-preparing activity, as well as for teachers' mutual development. 0 0