A Semi-Supervised Method to Learn and Construct Taxonomies Using the Web

Although many algorithms have been developed to harvest lexical resources, few organize the mined terms into taxonomies. We propose (1) a semi-supervised algorithm that uses a root concept, a basic level concept, and recursive surface patterns to learn automatically from the Web hyponym-hypernym pairs subordinated to the root; (2) a Web based concept positioning procedure to validate the learned pairs' is-a relations; and (3) a graph algorithm that derives from scratch the integrated taxonomy structure of all the terms. Comparing results with WordNet, we find that the algorithm misses some concepts and links, but also that it discovers many additional ones lacking in WordNet. We evaluate the taxonomization power of our method on reconstructing parts of the WordNet taxonomy. Experiments show that starting from scratch, the algorithm can reconstruct 62% of the WordNet taxonomy for the regions tested.

• Type: Journal
• Year: 2010
• Where: EMNLP
• Authors: Zornitsa Kozareva, Eduard H. Hovy
• Keywords: Algorithm, EMNLP 2010, Natural Language Processing, WordNet, WordNet Taxonomy