Extractors are boolean functions that allow, in some precise sense, extraction o f randomness from somewhat random distributions. Extractors, and the closely related “Dispersers”, exhibit some of the most “random-like” properties of explicitly constructed combinatorial structures. In turn, extractors and dispersers have many applications in “removing randomness” in various settings, and in maki...

Text mining for semi-structured documents requires information extractors. Programming extractors by hand is diicult to catch up with the amount and the variation of the documents placed on the WorldWide Web everyday. This paper presents our recent result on applying machine learning techniques to au-tomatize the generation of the extractors. Our goal is to develop a domain and language indepen...

This paper proposes feature augmentation methods using unlabeled data and several Named Entity (NE) extractors. We collect NE-related information of each word (which we call NE-related labels) from unlabeled data by using NE extractors. NE-related labels which we collect include candidate NE class labels of each word and NE class labels of co-occurring words. To accurately collect the NE-relate...

We present positive and negative results of fuzzy extractors with computational security. As a negative result, we show that, under a certain computational condition, the existence of a computational fuzzy extractor implies the existence of an information-theoretic fuzzy extractor with slightly weaker parameters. The condition is that the generation procedure of the fuzzy extractor is efficient...