Overcoming the Brittleness Bottleneck using Wikipedia: Enhancing Text Categorization with Encyclopedic Knowledge
Evgeniy Gabrilovich and Shaul Markovitch. Overcoming the Brittleness Bottleneck using Wikipedia: Enhancing Text Categorization with Encyclopedic Knowledge. In Proceedings of the Twenty-First National Conference on Artificial Intelligence, 1301-1306 Boston, MA, 2006.
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 \emph{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 ``Wal-Mart 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? 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 \emph{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.
Keywords: Wikipedia, Feature Generation, Text Categorization, Information Retrieval, ESA, Explicit Semantic Analysis
Secondary Keywords:
Online version:
Bibtex entry:
@inproceedings{Gabrilovich:2006:OBB,
Author = {Evgeniy Gabrilovich and Shaul Markovitch},
Title = {Overcoming the Brittleness Bottleneck using Wikipedia: Enhancing Text Categorization with Encyclopedic Knowledge},
Year = {2006},
Booktitle = {Proceedings of the Twenty-First National Conference on Artificial Intelligence},
Pages = {1301--1306},
Address = {Boston, MA},
Url = {http://www.cs.technion.ac.il/~shaulm/papers/pdf/Gabrilovich-Markovitch-aaai2006.pdf},
Keywords = {Wikipedia, Feature Generation, Text Categorization, Information Retrieval, ESA, Explicit Semantic Analysis},
Secondary-keywords = {Common-sense Knowledge},
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
\emph{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 ``Wal-Mart 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? 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 \emph{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.
}
}