We propose a method for annotating postto-post discourse structure in online user forum data, in the hopes of improving troubleshooting-oriented information access. We introduce the tasks of: (1) post classification, based on a novel dialogue act tag set; and (2) link classification. We also introduce three feature sets (structural features, post context features and semantic features) and experiment with three discriminative learners (maximum entropy, SVM-HMM and CRF). We achieve abovebaseline results for both dialogue act and link classification, with interesting divergences in which feature sets perform well over the two sub-tasks, and go on to perform preliminary investigation of the interaction between post tagging and linking.
REF , introduce the tasks of: (1) post classification, based on a novel dialogue act tag set and (2) link classification.
Tagging and Linking Web Forum Posts
Proceedings of the Fourteenth Conference on Computational Natural Language Learning
Computer Science