A Supervised Learning Algorithm for Information Extraction from Textual Data

In this article we present a supervised learning algorithm for the discovery of finite state automata, in the form of regular expressions, in textual data. The automata generate languages that consist of various representations of features useful in information extraction. We have successfully applied this learning technique in the extraction of textual features from police incident reports [2]. In this article we present the result of the application of our algorithm in extraction of the `problem solved' in patents. The `problem solved' in a patent identifies the particular solution to an insufficiency in prior art that the patent addresses

A Software Infrastructure for Research in Textual Data Mining

Few tools exist that address the challenges facing researchers in the Textual Data Mining (TDM) field. Some are too specific to their application, or are prototypes not suitable for general use. More general tools often are not capable of processing large volumes of data

Neuronal Integration of Dynamic Sources: Bayesian Learning and Bayesian Inference

One of the brain’s most basic functions is integrating sensory data from diverse sources. This ability causes us to question whether the neural system is computationally capable of intelligently integrating data, not only when sources have known, fixed relative dependencies but also when it must determine such relative weightings based on dynamic conditions, and then use these learned weightings to accurately infer information about the world. We suggest that the brain is, in fact, fully capable of computing this parallel task in a single network and describe a neural inspired circuit with this property. Our implementation suggests the possibility that evidence learning requires a more complex organization of the network than was previously assumed, where neurons have different specialties, whose emergence brings the desired adaptivity seen in human online inference