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Case-based Reasoning in Text Document Classification

机译:文本文档分类中基于案例的推理

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摘要

This work investigates document classification in Case-Based Reasoning (CBR). The investigation is exemplified by the design and implementation of a system that uses the knowledge-intensive CBR framework Creek to categorize textual cases.The Information Extraction tool CORPORUM analyzes natural language text by extracting "light weight ontologies" consisting of key concepts and the links between them. The output delivered by CORPORUM has been the basis of text categorization in Creek. To find the category of an unknown text case, Creek compares it to a number of already categorized texts and outputs most similar. The calculation of similarity between textual cases has been done according to Creek's existing method. The implemented program is based on a study of Textual CBR and Information Extraction, as well as an analysis of Creek's representation and reasoning functionality. When testing the implemented system, we have observed that Creek and CORPORUM can cooperate in categorizing documents, even if their format of representing text cases is initially different. Because of differences in relation types, the general domain knowledge of Creek was not fully utilized during case matching. However, our results suggests that Creek will benefit greatly from using a text analysis tool such as CORPORUM for ontology building.
机译:这项工作调查基于案例的推理(CBR)中的文档分类。通过使用知识密集型CBR框架Creek对文本案例进行分类的系统的设计和实现来举例说明该调查。信息提取工具CORPORUM通过提取“轻量本体”来分析自然语言文本,该“轻量本体”由关键概念及其之间的联系组成。他们。 CORPORUM提供的输出一直是Creek中文本分类的基础。为了找到未知文本案例的类别,Creek将其与许多已经分类的文本进行比较,并输出最相似的文本。文本案例之间的相似性计算已根据Creek的现有方法进行。实施的程序基于对文本CBR和信息提取的研究,以及对Creek表示和推理功能的分析。在测试实现的系统时,我们观察到Creek和CORPORUM可以合作对文档进行分类,即使它们表示文本大小写的格式最初不同。由于关系类型的差异,在案例匹配期间未充分利用Creek的一般领域知识。但是,我们的结果表明,使用诸如CORPORUM之类的文本分析工具进行本体构建将使Creek受益匪浅。

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