Author: Dayne Freitag

This work applies boosted wrapper induction (BWI), a machine learning algorithm for information extraction from semi-structured documents, to the problem of named entity recognition.

We describe a technique of this nature called information theoretic co clustering and give results of a series of experiments built around it that demonstrate the main ingredients of lexical optimization.

We present a system for unsupervised tagging of words into classes produced by a distributional clustering technique called co-clustering.

We describe an algorithm that learns simple, low-coverage wrapper-like extraction patterns, which we then apply to conventional information extraction problems using boosting.

We address: modeling sequential data with HMMs, problems with previous methods: motivation, the maximum entropy Markov model, segmentation of FAQs: experiments and results.

This paper investigates whether a machine can automatically learn the task of finding, within a large collection of candidate responses, the answers to questions.

This paper advocates for the use of HMMs for information extraction.

We describe three different multistrategy approaches. Experiments on two IE domains a collection of electronic seminar announcements from a university computer science department and a set of newswire articles describing corporate acquisitions from the Reuters collection demonstrate the effectiveness of all three approaches.

We show how information extraction can be cast as a standard machine learning problem, and argue for the suitability of relational learning in solving it.

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.