III: Small: Understanding the Relevance of Text Passages

一些信息检索（IR）查询可以最好地用网页来回答，而另一些查询可以用单个事实或命名实体来回答。许多其他问题可以最好地用一段文字来回答，我们建议为这项任务开发新技术，这将是对当前最先进技术的重大改进。开发有效的文章检索将对搜索工具产生重大影响，因为它极大地扩展了可以使用从网络检索的文本文章直接回答的查询范围。这对于基于使用小屏幕或语音输出的有限输出带宽的移动搜索应用程序尤其重要。在这种情况下，使用段落减少输出量同时保持高度相关性的能力将是至关重要的。我们将研究在之前的研究中被忽视或只部分解决的研究问题，例如，对于某些查询，显示段落是否比文档更好的答案，预测哪些查询在段落级别上有好的答案，对段落进行排序以检索最佳答案，以及评估段落作为答案的有效性。为了解决这些问题，我们将开发新的检索模型，可以为不同的文本粒度（如句子和段落）定义和排序“答案”，与好的段落级答案相关的查询属性模型，以及区分话题性和信息内容的模型。理解文本段落的相关性还将涉及在段落粒度上获得新型的相关性评估，并开发新的评估指标，将相关性与结果输出的大小结合起来。欲了解更多信息，请参阅该项目的网站：http://ciir.cs.umass.edu/research/answer_passages。

Some information retrieval (IR) queries can be best answered with a web page, others can be answered with a single fact or named entity. Many other queries could best be answered with a text passage and we propose to develop new techniques for this task that will be a significant improvement on the current state-of-the-art. Developing effective passage retrieval would have a major effect on search tools by greatly extending the range of queries that could be answered directly using text passages retrieved from the web. This is particularly important for mobile search applications with limited output bandwidth based on using either a small screen or speech output. In this case, the ability to use passages to reduce the amount of output while maintaining high relevance will be critical. We will study research issues that have either been ignored, or only partially addressed, in prior research, such as showing whether passages be better answers than documents for some queries, predicting which queries have good answers at the passage level, ranking passages to retrieve the best answers, and evaluating the effectiveness of passages as answers. To address these issues, we will develop new retrieval models that can define and rank "answers" for different text granularities such as sentences and passages, models of query properties that are associated with good passage-level answers, and models that differentiate between topicality and information content. Understanding the relevance of text passages will also involve obtaining new types of relevance assessments at passage granularity, and developing new evaluation metrics that combine relevance with the size of the result output. For further information see the project web site at: http://ciir.cs.umass.edu/research/answer_passages.