Machine learning algorithms hold extraordinary promise, but the reality is that their success
depends entirely on the suitability of the data available. This book is about Ripple-Down …

This paper presents a new approach to building legal citation classification systems. Our
approach is based on Ripple-down Rules (RDR), an efficient knowledge acquisition …

Making responsible lending decisions involves many factors. There is a growing amount of
research on machine learning applied to credit risk evaluation. This promises to enhance …

In the early years of knowledge acquisition research there was considerable discussion
about the possibility of obtaining knowledge from experts, based on the ideas of situated …

The idea of automatic summarization dates back to 1958, when Luhn invented the “auto
abstract”(Luhn, 1958). Since then, many diverse automatic summarization approaches have …

Event data analysis is becoming increasingly of interest to academic researchers looking for
patterns in the data. Unlike domain experts working in large companies who have access to …

Abstract Named Entity Recognition (NER) is important for extracting information from highly
heterogeneous web documents. Most NER systems have been developed based on formal …

Abstract The World Wide Web contains a massive amount of information in unstructured
natural language and obtaining valuable information from informally written Web documents …

Extracting information from semistructured text has been studied only for limited domain
sources due to its heterogeneous formats. This paper proposes a Ripple-Down Rules (RDR) …

Similar entity search is the task of identifying entities that most closely resemble a given
entity (eg, a person, a document, or an image). Although many techniques for estimating …