Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Discourse segmentation of multi-party conversation
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Speech and Language Processing (2nd Edition)
Speech and Language Processing (2nd Edition)
Summarizing spoken and written conversations
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Document summarization using conditional random fields
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Exploiting conversation structure in unsupervised topic segmentation for emails
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Classifying dialogue acts in one-on-one live chats
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Generating and validating abstracts of meeting conversations: a user study
INLG '10 Proceedings of the 6th International Natural Language Generation Conference
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In this work, we present a method for classifying the quality of blog comments using Linear-Chain Conditional Random Fields (CRFs). This approach is found to yield high accuracy on binary classification of high-quality comments, with conversational features contributing strongly to the accuracy. We also present a new corpus of blog data in conversational form, complete with user-generated quality moderation labels from the science and technology news blog Slashdot.