基于自适应进化聚类算法STAR的概念层次结构形式上下文约简
Formal context reduction in deriving concept hierarchies from corpora using adaptive evolutionary clustering algorithm star
摘要：
自动化从语料库导出概念层次结构的过程是有益的，因为概念层次结构的手动构造通常是耗时且资源密集的过程。因此，从语料库中学习概念层次结构的整个过程包括一系列步骤：将文本解析成句子，将句子拆分，然后将其标记化。在lemmatization步骤之后，使用FCA提取对。然而，在正式语境中可能会有一些无趣的和错误的对。生成形式背景可能会导致一个耗时的过程，因此需要减少形式背景的大小，以删除不感兴趣和错误的对，从而花费更少的时间来提取概念格和概念层次。在此前提下，本研究的目的是提出两个框架：（1）一个框架，以审查目前的过程中派生的概念层次从语料库使用FCA;（2）框架，以减少形式背景歧义的第一个框架，使用自适应版本的ECA*。在这两个框架上，使用维基百科的385个样本语料进行实验，研究了形式背景的缩减，得到了概念格和概念层次。使用概念格不变量将所得到的形式背景格评估为标准格。相应地，这两个格之间的同态保持了89%的质量，从而产生的概念层次结构的基本的，和减少的概念格继承了标准的结构关系。自适应ECA* 检查其对应的四个基线算法，以测量具有不同密度（填充率）的随机数据集上的执行时间。结果表明，自适应ECA* 执行概念格的速度比其他提到的竞争技术在不同的填充率。
英文摘要：
It is beneficial to automate the process of deriving concept hierarchies from corpora since a manual construction of concept hierarchies is typically a time-consuming and resource-intensive process. As such, the overall process of learning concept hierarchies from corpora encompasses a set of steps: parsing the text into sentences, splitting the sentences and then tokenising it. After the lemmatisation step, the pairs are extracted using FCA. However, there might be some uninteresting and erroneous pairs in the formal context. Generating formal context may lead to a time-consuming process, so formal context size reduction is required to remove uninterested and erroneous pairs, taking less time to extract the concept lattice and concept hierarchies accordingly. In this premise, this study aims to propose two frameworks: (1) A framework to review the current process of deriving concept hierarchies from corpus utilising FCA; (2) A framework to decrease the formal contexts ambiguity of the first framework using an adaptive version of ECA*. Experiments are conducted by applying 385 sample corpora from Wikipedia on the two frameworks to examine the reducing size of formal context, which leads to yield concept lattice and concept hierarchy. The resulting lattice of formal context is evaluated to the standard one using concept lattice-invariants. Accordingly, the homomorphic between the two lattices preserves the quality of resulting concept hierarchies by 89% in contrast to the basic ones, and the reduced concept lattice inherits the structural relation of the standard one. The adaptive ECA* is examined against its four counterpart baseline algorithms to measure the execution time on random datasets with different densities (fill ratios). The results show that adaptive ECA* performs concept lattice faster than other mentioned competitive techniques in different fill ratios.
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