Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Dialogue act modeling for automatic tagging and recognition of conversational speech
Computational Linguistics
2D Conditional Random Fields for Web information extraction
ICML '05 Proceedings of the 22nd international conference on Machine learning
Enriching the knowledge sources used in a maximum entropy part-of-speech tagger
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
Detection of question-answer pairs in email conversations
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Finding question-answer pairs from online forums
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Modeling dialogue structure with adjacency pair analysis and hidden Markov models
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
Automatic extraction of advice-revealing sentences foradvice mining from online forums
Proceedings of the seventh international conference on Knowledge capture
Toward advice mining: conditional random fields for extracting advice-revealing text units
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
| Hi-index | 0.00 |
Online forums are becoming a popular resource in the state of the art question answering (QA) systems. Because of its nature as an online community, it contains more updated knowledge than other places. However, going through tedious and redundant posts to look for answers could be very time consuming. Most prior work focused on extracting only question answering sentences from user conversations. In this paper, we introduce the task of sentence dependency tagging. Finding dependency structure can not only help find answer quickly but also allow users to trace back how the answer is concluded through user conversations. We use linear-chain conditional random fields (CRF) for sentence type tagging, and a 2D CRF to label the dependency relation between sentences. Our experimental results show that our proposed approach performs well for sentence dependency tagging. This dependency information can benefit other tasks such as thread ranking and answer summarization in online forums.