III: Small: Understanding the Relevance of Text Passages

一些信息检索（IR）查询可以用网页来回答，其他的可以用单个事实或命名实体来回答。许多其他的查询可以最好地回答一个文本段落，我们建议开发新的技术，这将是一个显着的改进，对目前的国家的最先进的。开发有效的通道检索将有一个重大影响的搜索工具，大大扩展的范围内的查询，可以直接回答使用从网络上检索的文本段落。这对于基于使用小屏幕或语音输出的输出带宽有限的移动的搜索应用尤其重要。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。我们将研究被忽视的研究问题，或只有部分解决，在以前的研究，如显示是否段落是更好的答案比文件的一些查询，预测哪些查询有很好的答案，在通道水平，排名通道检索最佳答案，并评估的有效性通道作为答案。为了解决这些问题，我们将开发新的检索模型，可以定义和排名“答案”不同的文本粒度，如句子和段落，模型的查询属性，与良好的搜索级别的答案，和模型，区分时事性和信息内容。 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，以及开发将相关性与结果输出的大小联合收割机相结合的新评估指标。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.