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Evaluating Paragraph Retrieval for why-QA

机译:评估为什么要进行质量检查的段落检索

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We implemented a baseline approach to why-question answering based on paragraph retrieval. Our implementation incorporates the Q AP ranking algorithm with addition of a number of surface features (cue words and XML markup). With this baseline system, we obtain an accuracy-at-10 of 57.0% with an MRR of 0.31. Both the baseline and the proposed evaluation method are good starting points for the current research and for other researchers working on the problem of why-QA. We also experimented with the addition of smart question analysis features to our baseline system (answer type and informational value of the subject). This however did not give significant improvement to our baseline. In the near future, we will investigate what other linguistic features can facilitate re-ranking in order to increase accuracy.
机译:我们基于段落检索实现了基线方法来回答问题。我们的实现将QAP排名算法与其他表面特征(提示词和XML标记)相结合。使用此基准系统,我们在10时的准确度为57.0%,MRR为0.31。基线和拟议的评估方法都是当前研究以及从事为什么QA问题的其他研究人员的良好起点。我们还尝试将智能问题分析功能添加到我们的基准系统中(答案类型和主题的信息价值)。但是,这并没有明显改善我们的基准。在不久的将来,我们将研究哪些其他语言功能可以促进重新排名,以提高准确性。

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