Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
The role of context in question answering systems
CHI '03 Extended Abstracts on Human Factors in Computing Systems
Using machine learning techniques to interpret WH-questions
ACL '01 Proceedings of the 39th Annual Meeting on Association for Computational Linguistics
Interrogative reformulation patterns and acquisition of question paraphrases
PARAPHRASE '03 Proceedings of the second international workshop on Paraphrasing - Volume 16
Answering complex questions with random walk models
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Descriptive question answering in encyclopedia
ACLdemo '05 Proceedings of the ACL 2005 on Interactive poster and demonstration sessions
Understanding and summarizing answers in community-based question answering services
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Question pre-processing in a QA system on Internet discussion groups
SumQA '06 Proceedings of the Workshop on Task-Focused Summarization and Question Answering
Estimating continuous distributions in Bayesian classifiers
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
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User information need detection is a fundamental issue in automatic question answering systems. Based on real questions collected from on-line question answering communities, this paper proposes a three-level question type taxonomy to model user information need. The three levels are based on interrogative patterns, hidden user intentions and specific answer expectations. One question can have multiple types in level 2&3. Question type assignment of level 2&3 is subjective-orientated, and may vary between different users. Shallow lexical, syntactic and semantic features are used to model the inherent subjectivity of user intentions. Classification experiments are conducted on a corpus of real questions collected from the web. Different machine learning methods are employed. Experimental results are promising. This indicates the capability of modeling user information need and subjectivity statistically, and that strong correlations exist between question types of the same level.