AUTOMATIC BAYESIAN METHODS IN TEXT RETRIEVAL

贝叶斯文献检索模型有着悠久的理论历史， 但直到最近才被证明是可行的。 当前在线 检索系统是部分贝叶斯，我们已经开发了一个完整的 贝叶斯模型的基础上，聚类的概念，其中包括文件 长度和局部项频率，同时允许模型完全 贝叶斯。 这与部分 贝叶斯模型，其中局部权重被特别处理。 它确实 然而，允许人们看到相关性的实际对数几率预测。 这些超过了观察到的相关性对数几率13.1，这给出了一个 关于术语依赖性的有趣观点。 一个新的模型的基础上贝叶斯方法已经开发， 与G.索尔顿 理论 细节已经敲定。 文件必须按“真实的”索引 它们引用的对象，这些真实的对象成为 一个多层次的系统，称为特异性网络。 每个 层次结构由特异性运算符产生，并产生 对象从最普通的根开始， 当一个人向叶子前进时， 的物体 填充节点由文本项或短语表示。 可能 是任何一个物体的许多代表。 程序是用来写的 来创建和存储这些结构，最终存储的数据将 用于使索引过程半自动化。 两份文件是 根据它们之间的相关性来评定它们的相似性， 它们所引用的真实的对象。 该方法将使用 以人为本的材料，是为了 在线检索系统原始分数的概率缩放。

Bayesian models of document retrieval have a long theoretical history in the subject but have only recently proved practical. Our current online retrieval system is partially Bayesian and we have developed a fully Bayesian model based on cluster concepts which incorporates the document length and local term frequency while allowing the model to be completely Bayesian. This performs at the same basic level as the partially Bayesian model in which local weights are treated ad hoc. It does however allow one to see the actual log odds predictions of relevance. These exceed the observed log odds of relevance by 13.1 which gives an interesting perspective on term dependency. 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. 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 representatives of any single object. Programs are to be written to create and store these structures and eventually the stored data will be used to make the process of indexing semiautomatic. Two documents are to be rated as to their similarity depending on the relatedness of the real objects that they reference. The approach is to be tested using the humanly judged material that has been produced for the purpose of probability scaling of online retrieval system raw scores.