语义模糊和随机条形码在无监督文本分类中的应用
Semantic Term "Blurring" and Stochastic "Barcoding" for Improved Unsupervised Text Classification
摘要：
现代社会产生的大量文本数据使得按主题直观地对文本数据进行分组、分类或分类变得越来越重要，以便进行有效的检索和搜索。然而，文本数据的高维性和不精确性，或者更一般地说，语言作为一个整体，在尝试执行无监督文档聚类时被证明是具有挑战性的。在这篇论文中，我们提出了两个新的方法来提高无监督的文档聚类/分类的主题。第一是改进文档表示。我们希望利用“术语邻域”和“模糊”相邻术语的语义权重。这些邻域位于由“词嵌入”提供的语义空间中。第二种方法是基于我们认为的“随机条形码”或“S-条形码”模式进行聚类修正。文本数据本质上是高维的，然而聚类通常发生在低维表示空间中。我们的方法利用低维聚类结果作为初始的集群配置，并迭代修改的配置在高维空间。我们的实验结果表明，这两种方法如何提高文档聚类的质量。虽然这篇论文详细阐述了这两个新的概念贡献，但David Yan的联合论文详细介绍了我们为无监督文档分类开发的特征转换和软件架构。
英文摘要：
The abundance of text data being produced in the modern age makes it increasingly important to intuitively group, categorize, or classify text data by theme for efficient retrieval and search. Yet, the high dimensionality and imprecision of text data, or more generally language as a whole, prove to be challenging when attempting to perform unsupervised document clustering. In this thesis, we present two novel methods for improving unsupervised document clustering/classification by theme. The first is to improve document representations. We look to exploit "term neighborhoods" and "blur" semantic weight across neighboring terms. These neighborhoods are located in the semantic space afforded by "word embeddings." The second method is for cluster revision, based on what we deem as "stochastic barcoding", or "S- Barcode" patterns. Text data is inherently high dimensional, yet clustering typically takes place in a low dimensional representation space. Our method utilizes lower dimension clustering results as initial cluster configurations, and iteratively revises the configuration in the high dimensional space. We show with experimental results how both of the two methods improve the quality of document clustering. While this thesis elaborates on the two new conceptual contributions, a joint thesis by David Yan details the feature transformation and software architecture we developed for unsupervised document classification.
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。
