TY - GEN
T1 - Learning online discussion structures by conditional random fields
AU - Wang, Hongning
AU - Wang, Chi
AU - Zhai, Cheng Xiang
AU - Han, Jiawei
PY - 2011/1/1
Y1 - 2011/1/1
N2 - Online forum discussions are emerging as valuable information repository, where knowledge is accumulated by the interaction among users, leading to multiple threads with structures. Such replying structure in each thread conveys important information about the discussion content. Unfortunately, not all the online forum sites would explicitly record such replying relationship, making it hard for both users and computers to digest the information buried in a discussion thread. In this paper, we propose a probabilistic model in the Conditional Random Fields framework to predict the replying structure for a threaded online discussion. Different from previous replying relation reconstruction methods, most of which fail to consider dependency between the posts, we cast the problem as a supervised structure learning problem to incorporate the features capturing the structural dependency and learn their relationship. Experiment results on three different online forums show that the proposed method can well capture the replying structures in online discussion threads, and multiple tasks such as forum search and question answering can benefit from the reconstructed replying structures.
AB - Online forum discussions are emerging as valuable information repository, where knowledge is accumulated by the interaction among users, leading to multiple threads with structures. Such replying structure in each thread conveys important information about the discussion content. Unfortunately, not all the online forum sites would explicitly record such replying relationship, making it hard for both users and computers to digest the information buried in a discussion thread. In this paper, we propose a probabilistic model in the Conditional Random Fields framework to predict the replying structure for a threaded online discussion. Different from previous replying relation reconstruction methods, most of which fail to consider dependency between the posts, we cast the problem as a supervised structure learning problem to incorporate the features capturing the structural dependency and learn their relationship. Experiment results on three different online forums show that the proposed method can well capture the replying structures in online discussion threads, and multiple tasks such as forum search and question answering can benefit from the reconstructed replying structures.
KW - Replying relation reconstruction
KW - Structure learning
KW - Threaded discussion
UR - http://www.scopus.com/inward/record.url?scp=80052116108&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=80052116108&partnerID=8YFLogxK
U2 - 10.1145/2009916.2009976
DO - 10.1145/2009916.2009976
M3 - Conference contribution
AN - SCOPUS:80052116108
SN - 9781450309349
T3 - SIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval
SP - 435
EP - 444
BT - SIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval
PB - Association for Computing Machinery
T2 - 34th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2011
Y2 - 24 July 2011 through 28 July 2011
ER -