Details

Autor(en) / Beteiligte

• Tri, Nguyen Thanh
• Le, Nguyen Minh
• Shimazu, Akira
• Matsumoto, Yuji
• Sproat, Richard W.
• Zhang, Min
• Wong, Kam-Fai

Titel

Using Semi-supervised Learning for Question Classification

Ist Teil von

• Computer Processing of Oriental Languages. Beyond the Orient: The Research Challenges Ahead, 2006, p.31-41

Ort / Verlag

Berlin, Heidelberg: Springer Berlin Heidelberg

Erscheinungsjahr

2006

Link zum Volltext

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• This paper tries to use unlabelled in combination with labelled questions for semi-supervised learning to improve the performance of question classification task. We also give two proposals to modify the Tri-training which is a simple but efficient co-training style algorithm to make it more suitable for question data type. In order to avoid bootstrap-sampling the training set to get different sets for training the three classifiers, the first proposal is to use multiple algorithms for classifiers in Tri-training, the second one is to use multiple algorithms for classifiers in combination with multiple views. The modification prevents the error rate at the initial step from being increased and our experiments show promising results.