Automatic Bayesian Methods In Text Retrieval

一个基于贝叶斯方法的新模型与G. Salton的向量模型有有趣的联系。理论细节已经制定出来了。理想情况下，文档必须由它们所引用的“真实”对象进行索引，这些真实对象成为称为特定网络的多个层次系统中的节点。每个层次结构都由一个特异性操作符产生，并产生一个对象树，该树从最一般的根开始，随着向叶的进展而向更大的特异性移动。填充节点的对象由文本术语或短语表示。任何单一对象都可能有许多表示形式。如果每个文档都必须手工转换为适合于表示其中所讨论的对象的形式，那么所描述的模型的构建是劳动密集型的。因此，我们正在开发对象表示的自动提取方法。这将导致一个可处理的任务来表示文档。我们的主要工作是开发机器学习方法，帮助构建这里描述的层次结构。

A new model based on the Bayesian approach has been developed which has interesting connections with the vector models of G. Salton. Theoretical details have been worked out. Ideally documents must be indexed by the "real" objects that they refer to and these real objects become nodes in a system of multiple hierarchies called a specificity network. Each hierarchy is produced by a specificity operator and results in a tree of objects starting at the root with the most general and moving to greater specificity as one progresses towards the leaves. The objects which populate nodes are represented by textual terms or phrases. There may be many representations of any single object. The model described is labor intensive to construct if each document must be converted by hand to a form suitable to represent the objects discussed within it. Thus we are developing methods of automatic extraction of object representations. This will lead to a tractable task to represent documents. Our major effort is to develop machine learning methods that can aid in the construction of the hierarchies described here.