Automatic Bayesian Methods In Text Retrieval

发展了一个基于贝叶斯方法的新模型，该模型与G.Salton的矢量模型有有趣的联系。理论上的细节已经制定出来。理想情况下，文档必须通过它们引用的“真实”对象进行索引，并且这些真实对象成为称为特定网络的多层次系统中的节点。每个层次结构由特定运算符产生，并导致从最一般的根部开始的对象树，并随着向树叶的方向移动到更大的特定性。填充节点的对象由文本术语或短语表示。任何单个对象都可能有多种表示形式。如果每个文档都必须手动转换为适合表示其中讨论的对象的形式，则所描述的模型构建起来非常费力。因此，我们正在开发自动提取对象表示的方法。这将产生一个易于处理的任务来表示文档。我们的主要努力是开发机器学习方法，以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.