Overcoming the brittleness bottleneck using wikipedia: enhancing text categorization with encyclopedic knowledge

TLDR

Text categorization depends on background knowledge, yet current systems treat documents as bags of words and cannot exploit broader context, limiting their ability to understand references such as supply‑chain logistics or drug classes. The paper proposes algorithms that enable text categorization systems to incorporate encyclopedic knowledge. The authors enrich document representations by automatically linking words to Wikipedia concepts, creating a feature space that combines lexical tokens with encyclopedic concepts derived from the largest online encyclopedia. Experiments show that the knowledge‑enhanced representation substantially improves categorization accuracy across many datasets.

Abstract

When humans approach the task of text categorization, they interpret the specific wording of the document in the much larger context of their background knowledge and experience. On the other hand, state-of-the-art information retrieval systems are quite brittle--they traditionally represent documents as bags of words, and are restricted to learning from individual word occurrences in the (necessarily limited) training set. For instance, given the sentence supply chain goes real time, how can a text categorization system know that Wal-Mart manages its stock with RFID technology? And having read that Ciprofloxacin belongs to the quinolones group, how on earth can a machine know that the drug mentioned is an antibiotic produced by Bayer? In this paper we present algorithms that can do just that. We propose to enrich document representation through automatic use of a vast compendium of human knowledge--an encyclopedia. We apply machine learning techniques to Wikipedia, the largest encyclopedia to date, which surpasses in scope many conventional encyclopedias and provides a cornucopia of world knowledge. Each Wikipedia article represents a concept, and documents to be categorized are represented in the rich feature space of words and relevant Wikipedia concepts. Empirical results confirm that this knowledge-intensive representation brings text categorization to a qualitatively new level of performance across a diverse collection of datasets.