基于形式概念分析的文本概念层次学习
Learning Concept Hierarchies from Text Corpora using Formal Concept Analysis
摘要：
我们提出了一种新的方法来自动获取分类或概念层次结构的文本语料库。该方法是基于形式概念分析（FCA），主要用于数据分析的方法，即调查和处理明确给定的信息。我们遵循Harris分布假设，将特定术语的上下文建模为表示句法依赖关系的向量，该向量由语言分析器从文本语料库中自动获取。在此背景信息的基础上，FCA产生一个格，我们转换成一种特殊的偏序构成一个概念层次。该方法进行评估，通过比较所产生的概念层次结构与手工制作的两个领域的分类：旅游和金融。我们还直接比较我们的方法与层次凝聚聚类，以及作为分裂聚类算法的一个实例的双节KMeans。此外，我们调查的影响，使用不同的措施加权每个属性的贡献，以及应用特定的平滑技术，以应付数据稀疏。
英文摘要：
We present a novel approach to the automatic acquisition of taxonomies or concept hierarchies from a text corpus. The approach is based on Formal Concept Analysis (FCA), a method mainly used for the analysis of data, i.e. for investigating and processing explicitly given information. We follow Harris distributional hypothesis and model the context of a certain term as a vector representing syntactic dependencies which are automatically acquired from the text corpus with a linguistic parser. On the basis of this context information, FCA produces a lattice that we convert into a special kind of partial order constituting a concept hierarchy. The approach is evaluated by comparing the resulting concept hierarchies with hand-crafted taxonomies for two domains: tourism and finance. We also directly compare our approach with hierarchical agglomerative clustering as well as with Bi-Section-KMeans as an instance of a divisive clustering algorithm. Furthermore, we investigate the impact of using different measures weighting the contribution of each attribute as well as of applying a particular smoothing technique to cope with data sparseness.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
