Semantic Term "Blurring" and Stochastic "Barcoding" for Improved Unsupervised Text Classification
This addresses the problem of efficient text retrieval and search for users dealing with large volumes of unstructured data, though it appears incremental as it builds on existing clustering and embedding techniques.
The paper tackles the challenge of unsupervised document clustering in high-dimensional text data by introducing two novel methods: semantic term 'blurring' to improve document representations using word embeddings, and 'stochastic barcoding' for cluster revision, which iteratively refines low-dimensional clustering results in high-dimensional space, showing experimental improvements in clustering quality.
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.