利用遗传规划构建信息检索查询

Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence Pub Date : 1994-06-27 DOI:10.1109/ICEC.1994.349905

F. Petry, B. Buckles, T. Sadasivan, D. Kraft

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引用次数: 77

摘要

将遗传规划应用于信息检索系统，通过关联反馈改进布尔查询公式。这种方法结合了信息检索和遗传规划的概念。文档被视为索引项空间中的向量。布尔查询被看作是一棵解析树，是遗传编程意义上的生物体。通过遗传规划机制对查询进行修改，提高查询的查准率和查全率。在某种程度上，相关反馈是通过用户在一组试用文档上定义的度量来合并的。候选查询的适应度可以直接表示为检索集相关性的函数。给出了基于试验台的初步结果。适应度函数的形式对性能有重要影响，适当的适应度函数考虑了基于话题性(也许还有其他因素)的相关性。

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The use of genetic programming to build queries for information retrieval

Genetic programming is applied to an information retrieval system in order to improve Boolean query formulation via relevance feedback. This approach brings together the concepts of information retrieval and genetic programming. Documents are viewed as vectors in index term space. A Boolean query, viewed as a parse tree, is an organism in the genetic programming sense. Through the mechanisms of genetic programming, the query is modified in order to improve precision and recall. Relevance feedback is incorporated, in part, via user defined measures over a trial set of documents. The fitness of a candidate query can be expressed directly as a function of the relevance of the retrieved set. Preliminary results based on a testbed are given. The form of the fitness function has a significant effect upon performance and the proper fitness functions take into account relevance based on topicality (and perhaps other factors).<>

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Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence

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