Answering Yes-No Questions by Penalty Scoring in History Subjects of University Entrance Examinations
Answering Yes-No Questions by Penalty Scoring in History Subjects of University Entrance Examinations
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发表时间：
2016
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通讯作者：
Yoshinobu Kano
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作者：
Yoshinobu Kano
回答是-否问题比简单地检索排名搜索结果更困难。要回答是非问题，特别是当正确答案是否定的，必须找到一个令人反感的关键字，使问题的答案是否定的。现有的系统，如基于事实的，不能回答是非问题很好，因为这种令人反感的关键字处理不足。我们建议一个算法，回答是-否的问题，通过分配一个重要的令人反感的关键字。具体地说，我们建议一个惩罚性的评分方法，发现并降低分数的文件，包括这些令人反感的关键字的部分。我们检查文档的每个部分（如段落）的关键字分布，计算关键字密度作为基本分数。然后，当关键字没有出现在目标部分，但出现在文档的其他部分时，我们使用一个令人反感的关键字惩罚。我们的算法是开放领域的问题是强大的，因为它不需要训练。我们在F1成绩中取得了比NTCIR-10 RITE 2共享任务最好成绩高出4.45分的成绩，并在2014年东大机器人项目模拟大学考试挑战中取得了最好成绩。
Answering yes–no questions is more difficult than simply retrieving ranked search results. To answer yes–no questions, especially when the correct answer is no, one must find an objectionable keyword that makes the question’s answer no. Existing systems, such as factoid-based ones, cannot answer yes–no questions very well because of insufficient handling of such objectionable keywords. We suggest an algorithm that answers yes–no questions by assigning an importance to objectionable keywords. Concretely speaking, we suggest a penalized scoring method that finds and makes lower score for parts of documents that include such objectionable keywords. We check a keyword distribution for each part of a document such as a paragraph, calculating the keyword density as a basic score. Then we use an objectionable keyword penalty when a keyword does not appear in a target part but appears in other parts of the document. Our algorithm is robust for open domain problems because it requires no training. We achieved 4.45 point better results in F1 scores than the best score of the NTCIR-10 RITE2 shared task, also obtained the best score in 2014 mock university examination challenge of the Todai Robot project.