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Automatically Extracting High-Quality Negative Examples for Answer Selection in Question Answering
Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval - SIGIR '17
We propose a heuristic called "one answer per document" for automatically extracting high-quality negative examples for answer selection in question answering. Starting with a collection of question-answer pairs from the popular TrecQA dataset, we identify the original documents from which the answers were drawn. Sentences from these source documents that contain query terms (aside from the answers) are selected as negative examples. Training on the original data plus these negative examplesdoi:10.1145/3077136.3080645 dblp:conf/sigir/ZhangRLS17 fatcat:z5r5kajjabggxdfo7camii7pvy