III: Small: Understanding the Relevance of Text Passages

一些信息检索(IR)查询可以用一个网页回答得最好，其他的可以用一个事实或命名实体来回答。许多其他问题可以通过文本段落得到最好的回答，我们建议为这项任务开发新的技术，这将是对当前最先进技术的重大改进。开发有效的段落检索将对搜索工具产生重大影响，因为它将极大地扩大可以使用从网络检索的文本段落直接回答的查询范围。这对于输出带宽有限的基于小屏幕或语音输出的移动搜索应用程序尤其重要。在这种情况下，在保持高度相关性的同时使用段落来减少输出量的能力将是至关重要的。我们将研究在以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是更好的答案，预测哪些查询在段落级别具有良好的答案，对段落进行排名以检索最佳答案，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，该模型可以为不同的文本粒度(如句子和段落)定义“答案”并对其进行排序，与良好的段落级别答案相关联的查询属性模型，以及区分主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类型的相关性评估，并制定将相关性与结果输出的大小相结合的新的评估指标。欲了解更多信息，请访问项目网站：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.