Development of a Coding Supporting System for Responses to Open-ended Questions with Applying Natural Language Processing Technique

主要的研究结果是，我们几乎已经完成了编码器的NANACO系统，它支持在社会调查中的职业编码。在职业编码方面，对有关受访者职业的开放式问题的答复必须归类到近200个职业编码中的一个，以便进行统计分析，这很费时，有时还会分类错误。首先，NANACO系统在编码器的显示器上呈现5个职业代码，这些职业代码是通过机器学习（支持向量机）和手工规则自动分类的。第二，NANACO系统还提供了关于答复者的信息，如年龄、性别和教育背景，这有助于编码者理解答复。第三，NANACO系统具有各种功能，如一个“字典”的视图窗口，它定义了每个职业代码，编码器工作方便。NANACO系统将包括编码员设置的职业代码在内的数据精细地保存为逗号分隔值（CSV）文件。此外，在JGSS（日本综合社会调查）或SSM（社会分层和社会流动性）调查等社会调查中使用NANACO系统的编码人员要求我们提供一个置信度度量，一个置信度度量，用于他们信心决策的第一位候选人。因此，我们提出了一种新的方法来估计置信度的措施，类成员概率，通过使用不仅是第一类的分数，但也从分类器的第二类分数。我们表明，该方法比以前的方法更有效。在未来的工作中，我们将设置类成员概率到NANANACO系统。很明显，NANACO系统可以很容易地扩展到对具有预定义类别的开放式问题的任何回答。

The main research result is that we have almost completed for coders the NANACO system, which supports an occupation coding in social surveys. In the occupation coding, responses to open-ended questions about respondents' occupation have to been classified into one of nearly 200 occupation codes for statistical analysis, which is time consuming and sometimes misclassified. First the NANACO system presents 5 occupation codes automatically classified with machine learning (support vector machines) and hand-crafted rules as candidates on coder's monitor. Second the NANACO system also presents information about respondents such as age, sex and educational background which help for coders to understand responses. Third the NANACO system has various functions such as a view window for a "dictionary" which defines each occupation code for coder to work easily. Finely the NANACO system saves data including occupation codes set by coders as a Comma Separated Values (CSV) file. Furthermore coders who used the NANACO system in social surveys such as JGSS (Japanese General Social Surveys) or SSM (Social Stratification and social Mobility) survey, asked us to supply a measure of confidence a measure of confidence for the first-ranked candidate of their confidence decisions. Therefore, we proposed a new method to estimate a measure of confidence, class membership probability, by using not only the first-class score but also the second-class score from a classifier. We showed that the proposed method was more effective than previous methods. In future work, we will set class membership probabilities into the NANACO system. It is clear that the NANACO system can be easily extended for any responses to open-ended questions which have predefined categories.