ABSTRACT:

We describe research to develop a never-ending language learner that runs 24 hours 
per day, forever, and that each day has two goals.  The first is to extract more 
information from the web to populate its growing knowledge base of structured knowledge.  
The second is to learn to read better than yesterday, as evidenced by its ability to 
go back to the same web pages it read yesterday, and extract more facts more accurately 
today. This research project is both a case study in how we might design an architecture 
for never-ending learning, and also an attempt at a new approach to natural language 
processing.  This talk will describe our approach, and experimental results from our NELL 
system which has been running nonstop since January 2, 2010.  As of April, it had extracted 
a structured knowledge base containing approximately a third of a million beliefs.  You can 
track its progress at http://rtw.ml.cmu.edu/readtheweb.html