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Inferential Language Models for Information Retrieval

机译：信息检索的推理语言模型

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摘要

Language modeling (LM) has been widely used in IR in recent years. An important operation in LM is smoothing of the document language model. However, the current smoothing techniques merely redistribute a portion of term probability according to their frequency of occurrences only in the whole document collection. No relationships between terms are considered and no inference is involved. In this article, we propose several inferential language models capable of inference using term relationships. The inference operation is carried out through a semantic smoothing either on the document model or query model, resulting in document or query expansion. The proposed models implement some of the logical inference capabilities proposed in the previous studies on logical models, but with necessary simplifications in order to make them tractable. They are a good compromise between inference power and efficiency. The models have been tested on several TREC collections, both in English and Chinese. It is shown that the integration of term relationships into the language modeling framework can consistently improve the retrieval effectiveness compared with the traditional language models. This study shows that language modeling is a suitable framework to implement basic inference operations in IR effectively.
机译：近年来，语言建模（LM）已在IR中广泛使用。 LM中的一项重要操作是平滑文档语言模型。但是，当前的平滑技术仅根据术语概率的出现频率仅在整个文档集合中重新分配了一部分。没有考虑术语之间的关系，也没有涉及推理。在本文中，我们提出了几种能够使用术语关系进行推理的推理语言模型。通过对文档模型或查询模型进行语义平滑来执行推理操作，从而导致文档或查询扩展。提出的模型实现了先前关于逻辑模型的研究中提出的一些逻辑推理能力，但是为了使其易于处理而进行了必要的简化。它们是推理能力和效率之间的良好折衷。该模型已在多个TREC藏书中进行了中英文测试。结果表明，与传统语言模型相比，将术语关系集成到语言建模框架中可以持续提高检索效率。这项研究表明，语言建模是有效地在IR中实现基本推理操作的合适框架。

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