TY - GEN
T1 - Enhancing accessibility of microblogging messages using semantic knowledge
AU - Hu, Xia
AU - Tang, Lei
AU - Liu, Huan
PY - 2011
Y1 - 2011
N2 - The volume of microblogging messages is increasing exponentially with the popularity of microblogging services. With a large number of messages appearing in user interfaces, it hinders user accessibility to useful information buried in disorganized, incomplete, and unstructured text messages. In order to enhance user accessibility, we propose to aggregate related microblogging messages into clusters and automatically assign them semantically meaningful labels. However, a distinctive feature of microblogging messages is that they are much shorter than conventional text documents. These messages provide inadequate term co occurrence information for capturing semantic associations. To address this problem, we propose a novel framework for organizing unstructured microblogging messages by transforming them to a semantically structured representation. The proposed framework first captures informative tree fragments by analyzing a parse tree of the message, and then exploits external knowledge bases (Wikipedia and WordNet) to enhance their semantic information. Empirical evaluation on a Twitter dataset shows that our framework significantly outperforms existing state-of-the-art methods.
AB - The volume of microblogging messages is increasing exponentially with the popularity of microblogging services. With a large number of messages appearing in user interfaces, it hinders user accessibility to useful information buried in disorganized, incomplete, and unstructured text messages. In order to enhance user accessibility, we propose to aggregate related microblogging messages into clusters and automatically assign them semantically meaningful labels. However, a distinctive feature of microblogging messages is that they are much shorter than conventional text documents. These messages provide inadequate term co occurrence information for capturing semantic associations. To address this problem, we propose a novel framework for organizing unstructured microblogging messages by transforming them to a semantically structured representation. The proposed framework first captures informative tree fragments by analyzing a parse tree of the message, and then exploits external knowledge bases (Wikipedia and WordNet) to enhance their semantic information. Empirical evaluation on a Twitter dataset shows that our framework significantly outperforms existing state-of-the-art methods.
KW - accessibility
KW - clustering
KW - labeling
KW - microblogging
UR - http://www.scopus.com/inward/record.url?scp=83055179194&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=83055179194&partnerID=8YFLogxK
U2 - 10.1145/2063576.2063993
DO - 10.1145/2063576.2063993
M3 - Conference contribution
AN - SCOPUS:83055179194
SN - 9781450307178
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 2465
EP - 2468
BT - CIKM'11 - Proceedings of the 2011 ACM International Conference on Information and Knowledge Management
T2 - 20th ACM Conference on Information and Knowledge Management, CIKM'11
Y2 - 24 October 2011 through 28 October 2011
ER -