Semantic Transforms Using Collaborative Knowledge Bases

5 Pages Posted: 30 Sep 2012 Last revised: 26 Nov 2013

Yegin Genc

Stevens Institute of Technology - School of Business

Winter Mason

Facebook; Stevens Institute of Technology - School of Business

Jeffrey V. Nickerson

Stevens Institute of Technology - School of Business

Date Written: November 7, 2013

Abstract

Topic models are used to classify documents, and they do so by designating sets of keywords that describe ideas, leaving interpretation of these keywords to humans. In this paper, we create variants of topic models in which documents are classified by the Wikipedia page that they match best; in this way we generate human-understandable topic names – Wikipedia page titles. We tested our method on a dataset – ACM abstracts – that had been manually classified into topics by the papers' authors. Our results often matched the authors' classifications. Moreover, the topics identified are clearer than the LDA topic modeling results. Our technique may have application to many other types of texts, including social media.

Suggested Citation

Genc, Yegin and Mason, Winter and Nickerson, Jeffrey V., Semantic Transforms Using Collaborative Knowledge Bases (November 7, 2013). Howe School Research Paper No. 2013-23. Available at SSRN: https://ssrn.com/abstract=2154367 or http://dx.doi.org/10.2139/ssrn.2154367

Yegin Genc (Contact Author)

Stevens Institute of Technology - School of Business ( email )

Hoboken, NJ 07030
United States

Winter Mason

Facebook ( email )

1601 S. California Ave.
Palo Alto, CA 94304
United States

Stevens Institute of Technology - School of Business ( email )

Hoboken, NJ 07030
United States

Jeffrey V. Nickerson

Stevens Institute of Technology - School of Business ( email )

Hoboken, NJ 07030
United States

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