Author: Dayne Freitag

The field of information extraction (IE) is concerned with applying natural language processing (NLP) and information retrieval (IR) techniques to the automatic extraction of essential details from text documents. We are exploring the use of machine learning methods for IE.

We describe a wide range of such heuristics including a novel one inspired by reinforcement learning techniques for propagating rewards through a graph|which can be used to affect a search engine’s rankings.

We describe an information seeking assistant for the world wide web. This agent, called WebWatcher, interactively helps users locate desired information by employing learned knowledge about which hyperlinks are likely to lead to the target information.

We describe a learning apprentice system, called WebWatcher, which both performs the kind of indexing used by Web catalogers like Lycos, and attempts to exploit the two sources of knowledge listed above.

This paper describes the first implementation of WebWatcher, a Learning Apprentice for the World Wide Web.

Personal software assistants that help users with tasks like finding information, scheduling calendars, or managing workflow will require significant customization.

We examine five greedy hillclimbing procedures that search for attribute sets that generalize well with ID3/C4.5. Experiments suggest hillclimbing in attribute space can yield substantial improvements in generalization performance.