1 Never Ending Learning Tom M. Mitchell Justin Betteridge, Jamie Callan, Andy Carlson, William Cohen, Estevam Hruschka, Bryan Kisiel, Mahaveer Jain, Jayant Krishnamurthy, Edith Law, Thahir Mohamed, Mehdi Samadi, Burr Settles, Richard Wang, Derry Wijaya Machine Learning Department Carnegie Mellon University March 2011 Humans learn many things, for years, and become better learners over time Why not machines?2 Never Ending Learning Task: acquire a growing competence without asymptote • over years • multiple functions • where learning one thing improves ability to learn the next • acquiring data from humans, environment Many candidate domains: • Robots • Softbots • Game players • Tweeters NELL: Never-Ending Language Learner Inputs: • initial ontology • handful of examples of each predicate in ontology • the web • occasional interaction with human trainers The task: • run 24x7, forever • each day: 1. extract more facts from the web to populate the initial ontology 2. learn to read (perform #1) better than yesterday3 NELL: Never-Ending Language Learner Goal: • run 24x7, forever • each day: 1. extract more facts from the web to populate given ontology 2. learn to read better than yesterday Today… Running 24x7, since January, 12, 2010 Input: • ontology defining ~500 categories and relations • 10-20 seed examples of each • 500 million web pages (ClueWeb – Jamie Callan) Result: • continuously growing KB with >525,000 extracted beliefs NELL Today • http://rtw.ml.cmu.edu • eg., “Disney”, “Mets”, “IBM”, “Pittsburgh” …4 Semi-Supervised Bootstrap Learning Paris Pittsburgh Seattle Cupertino mayor of arg1 live in arg1 San Francisco Austin denial arg1 is home of traits such as arg1 it’s underconstrained!!!anxiety selfishness Berlin Extract cities: hard (underconstrained) semi-supervised learning problem Key Idea 1: Coupled semi-supervised training of many functions much easier (more constrained) semi-supervised learning problem person NP5 NP: person Type 1 Coupling: Co-Training, Multi-View Learning [Blum & Mitchell; 98] [Dasgupta et al; 01 ] [Ganchev et al., 08] [Sridharan & Kakade, 08] [Wang & Zhou, ICML10] NP: person Type 1 Coupling: Co-Training, Multi-View Learning [Blum & Mitchell; 98] [Dasgupta et al; 01 ] [Ganchev et al., 08] [Sridharan & Kakade, 08] [Wang & Zhou, ICML10]6 NP: person Type 1 Coupling: Co-Training, Multi-View Learning [Blum & Mitchell; 98] [Dasgupta et al; 01 ] [Ganchev et al., 08] [Sridharan & Kakade, 08] [Wang & Zhou, ICML10] team person athlete coach sport NP athlete(NP) person(NP) athlete(NP) NOT sport(NP) NOT athlete(NP) sport(NP) Type 2 Coupling: Multi-task, Structured Outputs [Daume, 2008] [Bakhir et al., eds. 2007] [Roth et al., 2008] [Taskar et al., 2009] [Carlson et al., 2009]7 team person NP: athlete coach sport NP text context distribution NP morphology NP HTML contexts Multi-view, Multi-Task Coupling coachesTeam(c,t) playsForTeam(a,t) teamPlaysSport(t,s) playsSport(a,s) NP1 NP2 Learning Relations between NP’s8 team coachesTeam(c,t) playsForTeam(a,t) teamPlaysSport(t,s) playsSport(a,s) person NP1 athlete coach sport team person NP2 athlete coach sport team coachesTeam(c,t) playsForTeam(a,t) teamPlaysSport(t,s) playsSport(a,s) person NP1 athlete coach sport team person NP2 athlete coach sport playsSport(NP1,NP2) athlete(NP1), sport(NP2) Type 3 Coupling: Argument Types ~1200 coupled functions in NELL9 Pure EM Approach to Coupled Training E: estimate labels for each function of each unlabeled example M: retrain all functions, using these probabilistic labels Scaling problem: • E step: 20M NP’s, 1014 NP pairs to label • M step: 50M text contexts to consider for each function 1010 parameters to retrain • even more URL-HTML contexts… NELL’s Approximation to EM E’ step: • Consider only a growing subset of the latent variable assignments – category variables: up to 250 new NP’s per category per iteration – relation variables: add only if confident and args of correct type – this set of explicit latent assignments *IS* the knowledge base M’ step: • Each view-based learner retrains itself from the updated KB • “context” methods create growing subsets of contexts10 Learning and Function Execution Modules NELL Architecture Knowledge Base (latent variables) Text Context patterns (CPL) HTML-URL context patterns (SEAL) Morphology classifier (CML) Beliefs Candidate Beliefs Evidence Integrator Never-Ending Language Learning arg1_was_playing_arg2 arg2_megastar_arg1 arg2_icons_arg1 arg2_player_named_arg1 arg2_prodigy_arg1 arg1_is_the_tiger_woods_of_arg2 arg2_career_of_arg1 arg2_greats_as_arg1 arg1_plays_arg2 arg2_player_is_arg1 arg2_legends_arg1 arg1_announced_his_retirement_from_arg2 arg2_operations_chief_arg1 arg2_player_like_arg1 arg2_and_golfing_personalities_including_arg1 arg2_players_like_arg1 arg2_greats_like_arg1 arg2_players_are_steffi_graf_and_arg1 arg2_great_arg1 arg2_champ_arg1 arg2_greats_such_as_arg1 arg2_professionals_such_as_arg1 arg2_hit_by_arg1 arg2_greats_arg1 arg2_icon_arg1 arg2_stars_like_arg1 arg2_pros_like_arg1 arg1_retires_from_arg2 arg2_phenom_arg1 arg2_lesson_from_arg1 arg2_architects_robert_trent_jones_and_arg1 arg2_sensation_arg1 arg2_pros_arg1 arg2_stars_venus_and_arg1 arg2_hall_of_famer_arg1 arg2_superstar_arg1 arg2_legend_arg1 arg2_legends_such_as_arg1 arg2_players_is_arg1 arg2_pro_arg1 arg2_player_was_arg1 arg2_god_arg1 arg2_idol_arg1 arg1_was_born_to_play_arg2 arg2_star_arg1 arg2_hero_arg1 arg2_players_are_arg1 arg1_retired_from_professional_arg2 arg2_legends_as_arg1 arg2_autographed_by_arg1 arg2_champion_arg111 Coupled Training Helps! Using only two views: Text, HTML contexts. text HTML Coupled [Carlson et al., WSDM 2010] 10 iterations, 200 M web pages 44 categories, 27 relations 199 extractions per category PRECISION Text uncpl HTML uncpl Coupled Categories .41 .59 .90 Relations .69 .91 .95 If coupled learning is the key idea, how can we get new coupling constraints?12 Key Idea 2: Discover New Coupling Constraints • first order, probabilistic horn clause constraints: – connects previously uncoupled relation predicates – infers new beliefs for KB 0.93 athletePlaysSport(?x,?y)
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