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Computer Science @ Rochester
Friday, April 25, 2008
12:30 PM
CSB 703
Benjamin Van Durme
University of Rochester
Finding Cars, Goddesses and Enzymes & Open Knowledge Acquisition
I will discuss a simple, TFxIDF like method for acquiring large numbers of pairs of instances and class labels. For example, "go-karting" is-a "outdoor activity", or "alsike clover" is-a "forage". This method allows for the combination of clusters built using distributional similarity, plus imperfect lists of labelings derived via Hearst-like patterns, in a manner allowing for easy trade-off between precision and recall.

This research was done with Marius Pasca, while an intern at Google. Papers on this and related work will appear at this year's ACL and AAAI.

If there is time, I will follow with an overview of current work showing that so-called "old school" methods of extraction based on traditional NLP are competitive with state of the art systems recently developed within the community (where competing systems are built using shallow methods, giving results not useable for symbolic inference). In addition, we've we shown that our system, KNEXT, can produce the same sorts of "class attributes" that have thus far been best acquired through the use of commercial search engine query logs, a propriety resource currently unavailable to the research community.

This later research was done with Lenhart Schubert, and Ting Qian, with assistance in result evaluation provided by Daphne Liu and Matt Post.