A Trainable System for the Extraction of Meaning From Text
Abstract
This project is developing a trainable system that can extract meaning from
texts in different domains (example: various Internet newsgroups). The
system does partial parsing based on a large dictionary containing
approximately 150,000 words. The system assists the user in extracting a
semantic network representation for each member of a set of training articles
contained in some large database. Based on the user's training, the system
forms statistical tables, a knowledge base, and a set of rules mirroring the
user's actions. The system then generalizes these rules. Using
statistically-based semantic classification, the system applies these rules to
new articles from the database for automatically building semantic networks.
papers/bagga.ps
pdf/bagga.pdf
if/bagga
if/bagga/bagga.tex
[Papers]
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