Concept Learning and Categorization from the Web

In previous work, we found that a great deal of information about noun attributes can be extracted from the Web using simple text patterns, and that enriching vector-based models of concepts with this information about attributes led to drastic improvements in noun categorization. We extend this previous work in two ways: (i) by comparing concept descriptions extracted using patterns with descriptions extracted with a parser, and (ii) by developing an improved dataset balanced with respect to ambiguity, frequency, and WordNet unique beginners