According to common relevance-judgments regimes, such as TREC’s, a document can be deemed relevant to a query even if it contains a very short passage of text with pertinent information. This fact has motivated work on passage-based document retrieval: document ranking methods that induce information from the document’s passages. However, the main source of passage-based information utilized was passage-query similarities. In this paper, we address the challenge of utilizing richer sources of passage-based information to improve document retrieval effectiveness. Specifically, we devise a suite of learning-to-rank-based document retrieval methods that utilize an effective ranking of passages produced in response to the query. Some of the methods quantify the ranking of the passages of a document. Others utilize the feature-based representation of the document’s passages. Empirical evaluation attests to the clear merits of our methods with respect to highly effective baselines. Our best performing method is based on learning a document ranking function using document-query features and passage-query features of the document’s passage most highly ranked; the passage-query features are those used to learn a highly effective passage ranker.

中文翻译：

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基于段落的方法来学习对文档进行排名

根据常见的相关性判断机制，例如TREC，即使文档中包含相关信息的文本非常短，也可以将其视为与查询相关的文档。这一事实激发了基于段落的文档检索的工作：从文档段落中获取信息的文档排名方法。但是，利用的基于段落的信息的主要来源是段落查询的相似性。在本文中，我们解决了利用丰富的基于段落的信息源来提高文档检索效率的挑战。具体来说，我们设计了一套基于等级的学习文档检索方法，该方法利用对查询产生的段落进行有效排名。一些方法量化了文档段落的等级。其他人则使用文档段落的基于特征的表示。经验评估证明了我们的方法在高效基准方面的明显优势。我们效果最好的方法是基于使用排名最高的文档段落的文档查询功能和段落查询功能来学习文档排名功能；段落查询功能是用于学习高效段落排名的功能。