Question Answering Based Assisted Decision for Electric Power Fault Diagnosis
| Autor: | Yuhuai Wang, Qiang Zhang, Quanye Jia |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Matching (statistics)
Information retrieval 020205 medical informatics Computer science business.industry media_common.quotation_subject Rank (computer programming) Information technology 02 engineering and technology Fault (power engineering) 03 medical and health sciences 0302 clinical medicine Knowledge extraction 0202 electrical engineering electronic engineering information engineering Question answering Task analysis Conversation 030212 general & internal medicine business media_common |
| Zdroj: | 2020 IEEE 5th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA). |
| Popis: | With the development of Information Technology, question answering approaches have been applied in assisted decision, which can assist custom service staffs to automatically answer some basic questions and therefore can greatly reduce the cost of system operation. In this paper, we propose a question answering based approach for electric power fault diagnosis. First, the question answer data is analyzed and preprocessed from the conversation of manually answering the possible causes of given power equipment faults for making sure that one question corresponds to multiple answers. Then, the question and answers at word-level are matched based on a general “compare-aggregate” framework. Based on the matching measure, we can further rank the answers and select the answer with the highest correlation as the most likely fault cause for assisted decision. The relevant experimental results show that the model achieves good results and finds the best answer to the question. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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