Utilizing Entity Relation to Bridge the Language Gap in Cross-Lingual Question Answering System
نویسندگان
چکیده
We describe University at Albany’s CLQA system and its performance in English-Chinese subtask evaluation in NTCIR-6 CLQA. Firstly we illustrate our submitted system, which was built in two weeks. (We had to finish our CLQA system in this time limit because we were late registered.) Then we would like to introduce the improved system which utilizes our ACE (Automatic Content Extraction) relation detection and recognition (RDR) system to help bridge the language gap when answering some types of questions. The experimental results show that our proposed method helps to improve the system performance in answering questions about some specific relation between entities.
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