Analysis of statistical question classification for fact-based questions

• Published in: Information retrieval (Boston) 2005-01, Vol.8 (3), p.481-504
• Main Authors: METZLER, Donald, CROFT, W. Bruce
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Classification; Error analysis; Exact sciences and technology; Information and communication sciences; Information processing and retrieval; Information retrieval; Information retrieval. Man machine relationship; Information science. Documentation; Machine learning; Questions; Research process. Evaluation; Sciences and techniques of general use; Semantics; Statistical process control; Studies
• ISSN: 1386-4564; 1573-7659
• DOI: 10.1007/s10791-005-6995-3

Question classification systems play an important role in question answering systems and can be used in a wide range of other domains. The goal of question classification is to accurately assign labels to questions based on expected answer type. Most approaches in the past have relied on matching questions against hand-crafted rules. However, rules require laborious effort to create and often suffer from being too specific. Statistical question classification methods overcome these issues by employing machine learning techniques. We empirically show that a statistical approach is robust and achieves good performance on three diverse data sets with little or no hand tuning. Furthermore, we examine the role different syntactic and semantic features have on performance. We find that semantic features tend to increase performance more than purely syntactic features. Finally, we analyze common causes of misclassification error and provide insight into ways they may be overcome.