Skip to Main Content
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.