Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
2D Conditional Random Fields for Web information extraction
ICML '05 Proceedings of the 22nd international conference on Machine learning
Thread detection in dynamic text message streams
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Building implicit links from content for forum search
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Expertise networks in online communities: structure and algorithms
Proceedings of the 16th international conference on World Wide Web
Discovering authorities in question answer communities by using link analysis
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Finding question-answer pairs from online forums
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
User grouping behavior in online forums
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
A classification-based approach to question answering in discussion boards
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Online community search using thread structure
Proceedings of the 18th ACM conference on Information and knowledge management
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Exploiting thread structures to improve smoothing of language models for forum post retrieval
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
MAP estimation via agreement on trees: message-passing and linear programming
IEEE Transactions on Information Theory
Complete-Thread extraction from web forums
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
Community answer summarization for multi-sentence question with group L1 regularization
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Mixed membership Markov models for unsupervised conversation modeling
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Patient-Centered information extraction for effective search on healthcare forum
SBP'13 Proceedings of the 6th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
Learning thread reply structure on patient forums
Proceedings of the 2013 international workshop on Data management & analytics for healthcare
Patient-centric, multi-role, and multi-dimension information exploration on online healthcare forums
Proceedings of the sixth workshop on Ph.D. students in information and knowledge management
A learning approach for email conversation thread reconstruction
Journal of Information Science
Topic segmentation and labeling in asynchronous conversations
Journal of Artificial Intelligence Research
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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 to for both users and computers to digest the information buried in a thread discussion. 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 thread 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 describing the structural dependency among the discussion content 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.