一种基于邮件列表的软件问答信息抽取方法

Mailing List Based QA Information Extraction Approach

开源项目通常会提供邮件列表来帮助用户更好地理解和使用开源项目。但由于邮件的数量巨大、邮件内容组织繁杂、问题不明确、答案定位困难等问题,用户在邮件查询过程中定位一个特定的软件问答信息要花费大量的时间和精力。为此,提出一种基于邮件列表的软件问答信息抽取方法。该方法通过对邮件的简单分类与标注,实现自动的问题句抽取和答案邮件选取,从而提升了用户进行邮件列表查询以及开源软件项目学习的效率。最后,通过实验验证了该方法的有效性。

Open source projects often provide mailing lists to help users better understand and use open source software. However,developers often spend a lot of time to retrieve the emails when they want to find a special answer, because there are a huge number of emails with unclear question and complex organization. User usually take a lot of email conversations before they get a right answer. In the paper, we proposed and implemented a question ＆ answer information extraction approach based on open source software＇s mailing list. It can automatically extract the question sentence and the corresponding best answer from the emails, which can help users search mailing list and learn open source software more effectively. We also did some experiments to verify the availability and the our approach.