1. Julius Information Extractor
June 14, 2006
Kyle Woodward
Lee-Ming Zen

2. The Problem
There is a lot of text and information out there, but not a whole lot of tagging. How can we extract information a user is interested in without knowing anything beforehand?

3. Approach Based upon AT&T system Additional features Web
Build up “spelling” and “context” rules
Iteratively learn new rules by labeling and examining labels by jumping from one set of rules to the other
Additional features
We used a fixed length prefix and suffix to augment the context
Substituted POS instead of a full grammar parse for context
Window bounds selection to determine tag size
Web
Use information from web search snippets

4. Rules
Rules are a set of features for a particular labeling with weights for each feature
e.g. allcap, contains, full-string, etc.

5. What’s Cool Generality GUI tools Works
No restrictions on the type of data it runs against
No preassumed notions about the domain
GUI tools
Labeler
Statistics viewer
Works
Works well on small data sets

6. What’s Not Fails at larger corpora
Generality tradeoff means not being able to exploit certain information
Web context does not necessarily help due to noise