Mining Hidden Concepts: Using Short Text Clustering and Wikipedia Knowledge
Authors | Cheng-Lin Yang Nuttakorn Benjamasutin Yun-Heh Chen-Burger |
|---|---|
Publication date | 2014 |
DOI | 10.1109/WAINA.2014.109 |
Links | Original |
Mining Hidden Concepts: Using Short Text Clustering and Wikipedia Knowledge - scientific work related to Wikipedia quality published in 2014, written by Cheng-Lin Yang, Nuttakorn Benjamasutin and Yun-Heh Chen-Burger.
Overview
In recent years, there has been a rapidly increasing use of social networking platforms in the forms of short-text communication. However, due to the short-length of the texts used, the precise meaning and context of these texts are often ambiguous. To address this problem, authors have devised a new community mining approach that is an adaptation and extension of text clustering, using Wikipedia as background knowledge. Based on this method, authors are able to achieve a high level of precision in identifying the context of communication. Using the same methods, authors are also able to efficiently identify hidden concepts in Twitter texts. Using Wikipedia as background knowledge considerably improved the performance of short text clustering.
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Wikipedia Quality
Yang, Cheng-Lin; Benjamasutin, Nuttakorn; Chen-Burger, Yun-Heh. (2014). "[[Mining Hidden Concepts: Using Short Text Clustering and Wikipedia Knowledge]]".DOI: 10.1109/WAINA.2014.109.
English Wikipedia
{{cite journal |last1=Yang |first1=Cheng-Lin |last2=Benjamasutin |first2=Nuttakorn |last3=Chen-Burger |first3=Yun-Heh |title=Mining Hidden Concepts: Using Short Text Clustering and Wikipedia Knowledge |date=2014 |doi=10.1109/WAINA.2014.109 |url=https://wikipediaquality.com/wiki/Mining_Hidden_Concepts:_Using_Short_Text_Clustering_and_Wikipedia_Knowledge}}
HTML
Yang, Cheng-Lin; Benjamasutin, Nuttakorn; Chen-Burger, Yun-Heh. (2014). "<a href="https://wikipediaquality.com/wiki/Mining_Hidden_Concepts:_Using_Short_Text_Clustering_and_Wikipedia_Knowledge">Mining Hidden Concepts: Using Short Text Clustering and Wikipedia Knowledge</a>".DOI: 10.1109/WAINA.2014.109.
