AUTOMATIC BAYESIAN METHODS IN TEXT RETRIEVAL

文献检索的贝叶斯模型有很长的理论历史 但这一主题直到最近才被证明是可行的。我们目前的在线 检索系统是部分贝叶斯的，我们已经开发了一个完整的 结合文档的基于聚类概念的贝叶斯模型 长度和局部项频率，同时允许模型完全 贝叶斯理论。这与部分执行相同的基本级别 贝叶斯模型，在该模型中，局部权重被特别处理。的确如此。 然而，允许人们查看相关性的实际对数赔率预测。 这比观察到的对数相关性赔率高出13.1，这给出了一个 关于术语依赖的有趣视角。 开发了一种基于贝叶斯方法的新模型，该模型具有 与G.Salton的矢量模型之间有趣的联系。理论上的 细节已经制定出来了。文档必须按“真实”索引 对象，而这些真实的对象将成为 多层次的系统称为专门性网络。每个人 层次结构由特定运算符生成，并生成 对象从最一般的根开始，并移动到更大 当一个人向树叶进发时，他的特异度。这些对象是 填充节点由文本术语或短语表示。可能会有 是任何单个物体的多个代表。程序是要编写的 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进行。有两份文件是 根据它们之间的关联性被评为相似性 它们引用的真实对象。该方法将使用 人类判断的材料，是为…而生产的 在线检索系统原始分数的概率标度。

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.