Abstract
Information retrieval (IR) models based on vector spaces have been investigated for a long time. Nevertheless, they have recently attracted much research interest. In parallel, context has been rediscovered as a crucial issue in information retrieval. This article presents a principled approach to modeling context and its role in ranking information objects using vector spaces. First, the article outlines how a basis of a vector space naturally represents context, both its properties and factors. Second, a ranking function computes the probability of context in the objects represented in a vector space, namely, the probability that a contextual factor has affected the preparation of an object.
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Science Applications,General Business, Management and Accounting,Information Systems
Reference23 articles.
1. A theory of concepts and their combinations II: A Hilbert space representation
2. Bruza P. and Cole R. 2005. Quantum logic of semantic space: An exploratory investigation of context effects in practical reasoning. In We Will Show Them! Essays in Honour of Dov Gabbay vol. 1. College Publications UK 339--362. Bruza P. and Cole R. 2005. Quantum logic of semantic space: An exploratory investigation of context effects in practical reasoning. In We Will Show Them! Essays in Honour of Dov Gabbay vol. 1. College Publications UK 339--362.
Cited by
65 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献