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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Mollaei, A. Rahati-Quchani, S. Estaji, A. |
| Copyright Year | 2012 |
| Description | Author affiliation: Islamic Azad University, Mashhad, Iran (Mollaei, A.; Rahati-Quchani, S.) || Ferdowsi University of Mashhad, Mashhad, Iran (Estaji, A.) |
| Abstract | The question classification system is one of the important subsystems in the Question Answering Systems (QAS). In such systems through retrieval methods and information extraction the texts are retrieved in order to get to a correct answer. The current study is designed to present the architecture of question classification (QC) in Persian based on the Conditional Random Fields (CRF) machine learning model and evaluate effects of various features on its accuracy. In this study, sentences were classified into two levels of coarse and fine classes based on the type of the answer to each question. After extracting features and setting sliding window on the CRF model, CRF question classifier (QC) is train. Then, the QC predicts labels for every token in question. Next, a majority voting on the question classification output, is used to extract a unique label for each question. Further, the effects of different features on the ultimate accuracy of the system were evaluated. Finally results of this question classifier, illustrate a satisfactory accuracy. |
| Starting Page | 295 |
| Ending Page | 300 |
| File Size | 635810 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781467344753 |
| e-ISBN | 9781467344760 |
| DOI | 10.1109/ICCKE.2012.6395395 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-10-18 |
| Publisher Place | Iran |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Training Accuracy Question classification Question answering system Semantics Hidden Markov models Machine learning Cities and towns Feature extraction Conditional random fields Majority voting |
| Content Type | Text |
| Resource Type | Article |
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