Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Deriving marketing intelligence from online discussion
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications)
Comparisons of sequence labeling algorithms and extensions
Proceedings of the 24th international conference on Machine learning
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
Activity recognition using semi-Markov models on real world smart home datasets
Journal of Ambient Intelligence and Smart Environments
Detecting experiences from weblogs
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Sentence dependency tagging in online question answering forums
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Proceedings of the 21st ACM international conference on Information and knowledge management
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Web forums often contain explicit key learnings gleaned from people's experiences since they are platforms for personal communications on sharing information with others. One of the key learnings contained inWeb forums is often expressed in the form of advice. As part of human experience mining from Web resources, we aim to provide a methodology to extract advice-revealing sentences from Web forums due to its usefulness, especially in travel domain. Instead of viewing the problem as a simple classification, we define it as a sequence labeling problem using various features. We identify three different types of features (i.e., syntactic features, context features, and sentence informativeness) and propose a new way of using Hidden Markov Model (HMM) for labeling sequential sentences, which in our experiment gave the best performance for our task. Moreover, the sentence informativeness score serves as an important feature for this task. It is worth noting that this work is the first attempt to extract advice-revealing sentences from Web forums.