N EVER -E NDING L ANGUAGE L EARNING (NELL) Jacqueline DeLorie.

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Presentation transcript:

N EVER -E NDING L ANGUAGE L EARNING (NELL) Jacqueline DeLorie

W HAT IS NELL? An independent and never-ending machine semi- supervised learning system. Semi-supervised – “seed data” + unlabeled items A knowledge base of information built by reading the web. Categorizations and relation functions. Fruit(Apple). LocatedIn(Boston, Massachusetts) !Fruit(Carrot)

W HAT IS NELL’ S G OAL ? Answer questions posed by humans with no human intervention. Learn better over time, with little to no human interaction. Given what we have extracted today, learn to find facts and map them to categories better.

W HO MADE NELL? A research group at Carnegie Mellon University Lead by Professor Tom Mitchell Launched in January 2010 Sponsors: DARPA, NSF, Google, Yahoo! Uses the ClueWeb09 toolkit to parse neatly formatted webpages. ~500 Million webpages.

H OW D OES NELL W ORK ? Given an ontology, along with a few examples for each category (seed examples). Read the web, extract facts and map them to the ontology. Uses “bootstrapping”, train predictors using “seed” data, labels using predictors, trains predictors based on new labels, etc. Errors propagate causing a shift. Use combinations of predicates and negations of predicates to avoid.

O NTOLOGY NP(C) – set of valid nouns in corpus Patt(C) – set of valid category patterns in corpus NPr(C) - set of valid relations in corpus Pattr(C) - set of valid relation patterns in corpus

R ESULTS 6 Months after launch: For ¾ of relations and categories, 90-99% accuracy. For 1/4 of relations and categories, 25-60% accuracy. After that, weekly sessions held for human review of labels, intervening when blatant errors arose. Now, anyone can interact via NELL’s website:

R EFERENCES all/nell-computer-that-learns.shtml Ending_Language_Learning supervised_learning