Zheng Chen
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| Zheng Chen (Alternative names for this author) | |
| Affiliation | Unknown [+] |
| Country | Unknown [+] |
| Co-authors | Fred Lochovsky, Gang Wang, Hua Li, Hua-Jun Zeng, Jian Hu, Jian T. Sun, Lujun Fang, Pu Wang, Qiang Yang, Xiaochuan Ni, Yang Cao |
| Website | Unknown [+] |
| Statistics | |
| Authorship | Publications (5), datasets (0), tools (0) |
| Citations | Total (0), average (0), median (0), max (0), min (0) |
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Zheng Chen is an author.
Publications
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 |
|---|---|---|---|---|---|---|---|
| Cross lingual text classification by mining multilingual topics from wikipedia | Cross lingual text classification Multilingual Topic modeling Universal-topics Wikipedia |
WSDM | English | 2011 | 0 | 0 | |
| Mining multilingual topics from Wikipedia | English | 2009 | In this paper, we try to leverage a large-scale and multilingual knowledge base, Wikipedia, to help effectively analyze and organize Web information written in different languages. Based on the observation that one Wikipedia concept may be described by articles in different languages, we adapt existing topic modeling algorithm for mining multilingual topics from this knowledge base. The extracted 'universal' topics have multiple types of representations, with each type corresponding to one language. Accordingly, new documents of different languages can be represented in a space using a group of universal topics, which makes various multilingual Web applications feasible. | 0 | 0 | ||
| Understanding user's query intent with wikipedia | Query classification Query intent User intent Wikipedia |
World Wide Web | English | 2009 | 0 | 0 | |
| Using Wikipedia knowledge to improve text classification | Text classification Thesaurus Wikipedia |
Knowl. Inf. Syst. | English | 2009 | 0 | 0 | |
| Enhancing text clustering by leveraging Wikipedia semantics | English | 2008 | Most traditional text clustering methods are based on "bag of words" (BOW) representation based on frequency statistics in a set of documents. BOW, however, ignores the important information on the semantic relationships between key terms. To overcome this problem, several methods have been proposed to enrich text representation with external resource in the past, such as WordNet. However, many of these approaches suffer from some limitations: 1) WordNet has limited coverage and has a lack of effective word-sense disambiguation ability; 2) Most of the text representation enrichment strategies, which append or replace document terms with their hypernym and synonym, are overly simple. In this paper, to overcome these deficiencies, we first propose a way to build a concept thesaurus based on the semantic relations (synonym, hypernym, and associative relation) extracted from Wikipedia. Then, we develop a unified framework to leverage these semantic relations in order to enhance traditional content similarity measure for text clustering. The experimental results on Reuters and OHSUMED datasets show that with the help of Wikipedia thesaurus, the clustering performance of our method is improved as compared to previous methods. In addition, with the optimized weights for hypernym, synonym, and associative concepts that are tuned with the help of a few labeled data users provided, the clustering performance can be further improved. | 0 | 0 |
