NELL Computer Learns To Surf The Web For Facts

NELL is a Never-Ending Language Learner, a computer system created at Carnegie Mellon University. The intent is to design a machine learning system that is able to extract structured information from the wild and untamed (i.e., unstructured) World Wide Web.

NELL surfs the web even more than you do - 24 hours per day, every day, learning to read better each day. It tries to extract new instances of categories and relations. "In other words, [it tries to] find noun phrases that represent new examples of the input categories (e.g., "Barack Obama" is a person and politician), and find pairs of noun phrases that correspond to instances of the input relations (e.g., the pair "Jason Giambi" and "Yankees" is an instance of the playsOnTeam relation). These new instances are added to the growing knowledge base of structured beliefs."

The following table is taken from NELL's hundreds of thousands of facts about the Internet, in this case, music varieties. The number at the far left is NELL's confidence in the assessment; as you can see, NELL is 100% certain that these musical categories exist.


(NELL knowledgebase on music facts)

NELL has been in continuous operation since January 2010. For the first 6 months it was allowed to run without human supervision, learning to extract instances of a few hundred categories and relations, resulting in a knowledge base containing approximately a third of a million extracted instances of these categories and relations. At that point, it had improved substantially its ability to read three quarters of these categories and relations (with precision in the range 90% to 99%), but it had become inaccurate in extracting instances of the remaining fourth of the ontology (many had precisions in the range 25% to 60%).

The estimated precision of the beliefs it had added to its knowledge base at that point was 71%. We are still trying to understand what causes it to become increasingly competent at reading some types of information, but less accurate over time for others. Beginning in June, 2010, we began periodic review sessions every few weeks in which we would spend about 5 minutes scanning each category and relation. During this 5 minutes, we determined whether NELL was learning to read it fairly correctly, and in case not, we labeled the most blatant errors in the knowledge base. NELL now uses this human feedback in its ongoing training process, along with its own self-labeled examples. In July, a spot test showed the average precision of the knowledge base was approximately 87% over all categories and relations. We continue to add new categories and relations to the ontology over time, as NELL continues learning to populate its growing knowledge base.

NELL certainly has a wider knowledge of music than I have, based on the noun phrases shown above.

Science fiction has a number of examples of computer systems that try to learn more about human beings through analyzing data available on computer systems. One example that comes to mind is Mike from Robert Heinlein's 1966 novel The Moon is a Harsh Mistress:

When Mike was installed in Luna, he was pure thinkum, a flexible logic - "High-Optional, Logical, Multi-evaluating Supervisor, Mark IV, Mod. L" - a HOLMES FOUR. He computed ballistics for pilotless freighters and controlled their catapult. This kept him busy less than one percent of time and Luna Authority never believed in idle hands. They kept hooking hardware into him - decision-action boxes to let him boss other computers, bank on bank of additional memories, more banks of associational neural nets, another tubful of twelve-digit random numbers, a greatly augmented temporary memory.

Read more at CMU's Read the Web project; also, read the published paper on NELL Toward an Architecture for Never-Ending Language Learning.

Scroll down for more stories in the same category. (Story submitted 10/5/2010)

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