Distilling information from text: The EDS TemplateFiller System

Distilling Information from Text: The EDS TemplateFiller System

A system is described which digests large volumes of text, filtering out irrelevant articles and distilling the remainder into templates that represent information from the articles in simple slot/filler pairs. The system is highly modular in that it consists of a series of programs, each of which contributes information to the text to help in the final analysis of determining which strings con...

Distilling Reliable Information From Unreliable Theories

Suppose a domain expert gives us a domain theory which is meant to classify examples as positive or negative examples of some concept. Now suppose, as is often the case, that the expert speciies parts of the theory which might be in need of repair, as opposed to those parts of the theory which are certainly not in need of repair. We say that such a theory is partially mutable. There might be so...

Distilling Information Reliability and Source Trustworthiness from Digital Traces

Online knowledge repositories typically rely on their users or dedicated editors to evaluate the reliability of their content. These evaluations can be viewed as noisy measurements of both information reliability and information source trustworthiness. Can we leverage these noisy evaluations, often biased, to distill a robust, unbiased and interpretable measure of both notions? In this paper, w...

Distilling Entanglement from Fermions

Since Fermions are based on anti-commutation relations, their entanglement can not be studied in the usual way, such that the available theory has to be modified appropriately. Recent publications consider in particular the structure of separable and of maximally entangled states. In this talk we want to discuss local operations and entanglement distillation from bipartite, Fermionic systems. T...

Information Extraction from Text