C4.5: programs for machine learning
C4.5: programs for machine learning
Learning to resolve natural language ambiguities: a unified approach
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
A re-examination of text categorization methods
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
Hierarchical classification of Web content
SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Machine Learning
A Maximum-Entropy-Inspired Parser
A Maximum-Entropy-Inspired Parser
SNoW User Guide
Toward semantics-based answer pinpointing
HLT '01 Proceedings of the first international conference on Human language technology research
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
Parsing and question classification for question answering
ODQA '01 Proceedings of the workshop on Open-domain question answering - Volume 12
ILP'02 Proceedings of the 12th international conference on Inductive logic programming
A comparison of methods for multiclass support vector machines
IEEE Transactions on Neural Networks
Evaluating high accuracy retrieval techniques
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Learning query-class dependent weights in automatic video retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
QnA: augmenting an instant messaging client to balance user responsiveness and performance
CSCW '04 Proceedings of the 2004 ACM conference on Computer supported cooperative work
Analysis of Statistical Question Classification for Fact-Based Questions
Information Retrieval
Learning question classifiers: the role of semantic information
Natural Language Engineering
A language independent method for question classification
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Enhanced answer type inference from questions using sequential models
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Identifying and improving retrieval for procedural questions
Information Processing and Management: an International Journal
A machine learning approach for Indonesian question answering system
AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
Structure and semantics for expressive text kernels
Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
Exploring question subjectivity prediction in community QA
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Question classification with semantic tree kernel
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Kernel methods, syntax and semantics for relational text categorization
Proceedings of the 17th ACM conference on Information and knowledge management
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Question classification is very important for question answering. This paper presents our research work on automatic question classification through machine learning approaches. We have experimented with five machine learning algorithms: Nearest Neighbors (NN), Naive Bayes (NB), Decision Tree (DT), Sparse Network of Winnows (SNoW), and Support Vector Machines (SVM) using two kinds of features: bag-of-words and bag-of-ngrams. The experiment results show that with only surface text features the SVM outperforms the other four methods for this task. Further, we propose to use a special kernel function called the tree kernel to enable the SVM to take advantage of the syntactic structures of questions. We describe how the tree kernel can be computed efficiently by dynamic programming. The performance of our approach is promising, when tested on the questions from the TREC QA track.