Deep information retrieval - diving into information granularity and abstraction

深度信息检索——深入信息粒度和抽象

• 批准号: RGPIN-2018-05774
• 负责人: Nie, JianYun
• 金额: $ 3.5万
• 依托单位: Université de Montréal
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=713842
• 关键词: Deep; information; retrieval; diving; into

Although the final goal of information retrieval (IR) is to find relevant (pieces of) information, the research in IR has been generally limited to retrieving entire documents. In practice, many user queries are intended to find more precise segments of texts within documents. The first problem we deal with in this research is to make it possible for end users to find answers of different levels of granularity. IR also uses a unique representation for a document. In practice, user's search intents vary greatly: a query may be used to find a text containing a specific sequence of words, or a text containing the required semantics. The unique representation is unable to satisfy such various search intents. We propose to represent texts at different levels of abstraction, from surface words to more abstract semantic representations. Queries with different intents could then be compared with text representations at appropriate levels of abstraction. Finally, IR needs a complex ranking function, which is usually learned from ranking examples (learning-to-rank). However, the learning-to-rank approaches have never been used in combination with learning representations. We propose to combine the two learning tasks that are required. In addition, the matching function may also involve the use of domain knowledge to infer the semantic relations between a document and a query. The above problems will be investigated using deep learning techniques. Neural network representations for texts will be created at several layers, corresponding to different levels of abstraction. In addition, both the entire document and the searchable segments in it are represented. The learning-to-rank method will be trained to select the appropriate way to use the representations created in the network to rank documents for a query. The ultimate goal of this research is to develop more intelligent IR systems that can understand and cope with various user information needs.

尽管信息检索 (IR) 的最终目标是查找相关（部分）信息，但 IR 的研究通常仅限于检索整个文档。在实践中，许多用户查询旨在查找文档中更精确的文本片段。我们在这项研究中处理的第一个问题是让最终用户能够找到不同粒度级别的答案。 IR 还使用文档的唯一表示形式。在实践中，用户的搜索意图差异很大：查询可用于查找包含特定单词序列的文本，或包含所需语义的文本。独特的表示无法满足如此多样化的搜索意图。我们建议以不同的抽象级别表示文本，从表面单词到更抽象的语义表示。然后可以将具有不同意图的查询与适当抽象级别的文本表示进行比较。 最后，IR 需要一个复杂的排名函数，该函数通常是从排名示例中学习的（学习排名）。然而，学习排序方法从未与学习表示结合使用。我们建议将所需的两项学习任务结合起来。此外，匹配功能还可以涉及使用领域知识来推断文档和查询之间的语义关系。 将使用深度学习技术来研究上述问题。文本的神经网络表示将在多个层上创建，对应于不同的抽象级别。此外，还表示整个文档和其中的可搜索片段。学习排名方法将被训练以选择适当的方式来使用网络中创建的表示来对查询的文档进行排名。 这项研究的最终目标是开发更智能的红外系统，能够理解和应对各种用户信息需求。