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Never Ending Language Learning Carnegie Mellon University

Never Ending Language Learning Carnegie Mellon University. Tenet 1: Understanding requires a belief system We’ll never produce natural language understanding systems until we have systems that react to arbitrary sentences by saying one of: I understand, and already knew that

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Never Ending Language Learning Carnegie Mellon University

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  1. Never Ending Language Learning Carnegie Mellon University

  2. Tenet 1: Understanding requires a belief system We’ll never produce natural language understanding systems until we have systems that react to arbitrary sentences by saying one of: I understand, and already knew that I understand, and didn’t know, but accept it I understand, and disagree because …

  3. Tenet 2: We’ll never really understand learning until we build machines that learn many different things, over years, and become better learners over time.

  4. NELL: Never-Ending Language Learner Inputs: • initial ontology • few examples of each ontology predicate • the web • occasional interaction with human trainers The task: • run 24x7, forever • each day: • extract more facts from the web to populate the initial ontology • learn to read (perform #1) better than yesterday

  5. NELL today • Running 24x7, since January, 12, 2010 • Inputs: • ontology defining >600 categories and relations • 10-20 seed examples of each • 500 million web pages • 100,000 web search queries per day • ~ 5 minutes/day of human guidance • Result: • KB with > 15 million candidate beliefs, growing daily • learning to reason, as well as read • automatically extending its ontology

  6. NELL knowledge fragment football uses equipment climbing skates helmet Canada Sunnybrook Miller uses equipment city company hospital Wilson country hockey Detroit GM politician CFRB radio Pearson Toronto play hired hometown airport competeswith home town StanleyCup Maple Leafs city company Red Wings city stadium won won Toyota team stadium Connaught city paper league league acquired city stadium NHL Maple Leaf Gardens member Hino created plays in economic sector Globe and Mail Sundin Prius writer automobile Toskala Skydome Corrola Milson

  7. NELL Today • http://rtw.ml.cmu.edu follow NELL here • eg.“diabetes”, “Avandia”, ,“tea”, “IBM”, “love” “baseball” “BacteriaCausesCondition” …

  8. Semi-Supervised Bootstrap Learning it’s underconstrained!! Extract cities: San Francisco Austin denial anxiety selfishness Berlin Paris Pittsburgh Seattle Cupertino mayor of arg1 live in arg1 arg1 is home of traits such as arg1

  9. Key Idea 1: Coupled semi-supervised training of many functions person NP hard (underconstrained) semi-supervised learning problem much easier (more constrained) semi-supervised learning problem

  10. 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] person f1(NP) f3(NP) f2(NP) NP text context distribution NP morphology NP HTML contexts NP: www.celebrities.com: <li> __ </li> … __ is a friend rang the __ … __ walked in capitalized? ends with ‘...ski’? … contains “univ.”?

  11. 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] person sport athlete coach team athlete(NP)  person(NP) NP athlete(NP)  NOT sport(NP) NOT athlete(NP)  sport(NP)

  12. Multi-view, Multi-Task Coupling person sport athlete coach team NP text context distribution NP morphology NP HTML contexts NP:

  13. Learning Relations between NP’s playsSport(a,s) coachesTeam(c,t) playsForTeam(a,t) teamPlaysSport(t,s) NP1 NP2

  14. playsSport(a,s) coachesTeam(c,t) playsForTeam(a,t) teamPlaysSport(t,s) person sport person sport athlete athlete team coach team coach NP1 NP2

  15. Type 3 Coupling: Argument Types playsSport(NP1,NP2)  athlete(NP1), sport(NP2) playsSport(a,s) coachesTeam(c,t) playsForTeam(a,t) teamPlaysSport(t,s) person sport person sport athlete athlete team coach team coach over 2500 coupled functions in NELL NP1 NP2

  16. Basic NELL Architecture Knowledge Base (latent variables) Beliefs Evidence Integrator Candidate Beliefs Continually Learning Extractors Text Context patterns (CPL) HTML-URL context patterns (SEAL) Morphology classifier (CML)

  17. NELL: Learned reading strategies Plays_Sport(arg1,arg2): 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_arg1 …

  18. If coupled learning is the key,how can we get new coupling constraints?

  19. 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)  athletePlaysForTeam(?x,?z) teamPlaysSport(?z,?y)

  20. Example Learned Horn Clauses athletePlaysSport(?x,basketball)  athleteInLeague(?x,NBA) athletePlaysSport(?x,?y)  athletePlaysForTeam(?x,?z) teamPlaysSport(?z,?y) teamPlaysInLeague(?x,NHL)  teamWonTrophy(?x,Stanley_Cup) athleteInLeague(?x,?y) athletePlaysForTeam(?x,?z), teamPlaysInLeague(?z,?y) cityInState(?x,?y)  cityCapitalOfState(?x,?y), cityInCountry(?y,USA) newspaperInCity(?x,New_York)  companyEconomicSector(?x,media) generalizations(?x,blog) 0.95 0.93 0.91 0.90 0.88 0.62*

  21. Some rejected learned rules teamPlaysInLeague{?x nba}  teamPlaysSport{?x basketball} 0.94 [ 35 0 35 ] [positive negative unlabeled] cityCapitalOfState{?x ?y}  cityLocatedInState{?x ?y}, teamPlaysInLeague{?y nba} 0.80 [ 16 2 23 ] teamplayssport{?x, basketball}  generalizations{?x, university} 0.61 [ 246 124 3063 ]

  22. Learned Probabilistic Horn Clause Rules 0.93 playsSport(?x,?y)  playsForTeam(?x,?z), teamPlaysSport(?z,?y) playsSport(a,s) coachesTeam(c,t) playsForTeam(a,t) teamPlaysSport(t,s) person sport person sport athlete athlete team coach team coach NP1 NP2

  23. Key Idea 3: Automatically extend ontology

  24. Ontology Extension (1) [Mohamed et al., EMNLP 2011] • Goal: • Add new relations to ontology • Approach: • For each pair of categories C1, C2, • co-cluster pairs of known instances, and text contexts that connect them

  25. Example Discovered Relations [Mohamed et al. EMNLP 2011]

  26. NELL: recently self-added relations • athleteWonAward • animalEatsFood • languageTaughtInCity • clothingMadeFromPlant • beverageServedWithFood • fishServedWithFood • athleteBeatAthlete • athleteInjuredBodyPart • arthropodFeedsOnInsect • animalEatsVegetable • plantRepresentsEmotion • foodDecreasesRiskOfDisease • clothingGoesWithClothing • bacteriaCausesPhysCondition • buildingMadeOfMaterial • emotionAssociatedWithDisease • foodCanCauseDisease • agriculturalProductAttractsInsect • arteryArisesFromArtery • countryHasSportsFans • bakedGoodServedWithBeverage • beverageContainsProtein • animalCanDevelopDisease • beverageMadeFromBeverage

  27. Key Idea 4: Cumulative, Staged Learning Learning X improves ability to learn Y • Classify noun phrases (NP’s) by category • Classify NP pairs by relation • Discover rules to predict new relation instances • Learn which NP’s (co)refer to which concepts • Discover new relations to extend ontology • Learn to infer relation instances via targeted random walks • Learn to assign temporal scope to beliefs • Learn to microread single sentences • Vision: co-train text and visual object recognition • Goal-driven reading: predict, then read to corroborate/correct • Make NELL a conversational agent on Twitter • Add a robot body to NELL

  28. Learning to Reason at Scale football uses equipment climbing skates helmet Canada Sunnybrook Miller uses equipment city company hospital Wilson country hockey Detroit GM politician CFRB radio Pearson Toronto play hired hometown airport competeswith home town StanleyCup Maple Leafs city company Red Wings city stadium won won Toyota team stadium Connaught city paper league league acquired city stadium NHL Maple Leaf Gardens member Hino created plays in economic sector Globe and Mail Sundin Prius writer automobile Toskala Skydome Corrola Milson

  29. Inference by KB Random Walks [Lao et al, EMNLP 2011] If: competeswith (x1,x2) economic sector (x2, x3) x1 x2 x3 Then: economic sector (x1, x3)

  30. Inference by KB Random Walks [Lao et al, EMNLP 2011] KB: Random walk path type: competeswith economic sector ? ? ? Infer Pr(R(x,y)): Trained logistic function for R, where ith feature is probability of arriving at node y when starting at node x, and taking a random walk along path type i

  31. [Lao et al, EMNLP 2011] CityLocatedInCountry(Pittsburgh) = ? Pittsburgh Logistic RegresssionWeight Feature = Typed Path CityInState, CityInstate-1, CityLocatedInCountry 0.8 0.32 Feature Value

  32. [Lao et al, EMNLP 2011] CityLocatedInCountry(Pittsburgh) = ? Pennsylvania CityInState Pittsburgh Logistic RegresssionWeight Feature = Typed Path CityInState, CityInstate-1, CityLocatedInCountry 0.8 0.32 Feature Value

  33. [Lao et al, EMNLP 2011] CityLocatedInCountry(Pittsburgh) = ? Pennsylvania CityInState-1 CityInState CityInState-1 …(14) Pittsburgh Philadelphia Harisburg Logistic RegresssionWeight Feature = Typed Path CityInState, CityInstate-1, CityLocatedInCountry 0.8 0.32 Feature Value

  34. [Lao et al, EMNLP 2011] CityLocatedInCountry(Pittsburgh) = ? U.S. Pennsylvania CityLocatedInCountry CityInState-1 CityInState CityInState-1 …(14) Pittsburgh Philadelphia Harisburg Logistic RegresssionWeight Feature = Typed Path CityInState, CityInstate-1, CityLocatedInCountry 0.8 0.32 Feature Value

  35. [Lao et al, EMNLP 2011] CityLocatedInCountry(Pittsburgh) = ? U.S. Pennsylvania CityLocatedInCountry CityInState-1 CityInState CityInState-1 …(14) Pittsburgh Philadelphia Harisburg Pr(U.S. | Pittsburgh, TypedPath) Logistic RegresssionWeight Feature = Typed Path CityInState, CityInstate-1, CityLocatedInCountry 0.8 0.32 Feature Value

  36. [Lao et al, EMNLP 2011] CityLocatedInCountry(Pittsburgh) = ? U.S. Pennsylvania CityLocatedInCountry CityInState-1 CityInState CityInState-1 …(14) Pittsburgh Philadelphia Harisburg Logistic RegresssionWeight Feature = Typed Path CityInState, CityInstate-1, CityLocatedInCountry 0.8 0.32 AtLocation-1, AtLocation, CityLocatedInCountry 0.20 Feature Value

  37. [Lao et al, EMNLP 2011] CityLocatedInCountry(Pittsburgh) = ? U.S. Pennsylvania CityLocatedInCountry CityInState-1 CityInState CityInState-1 …(14) Pittsburgh Philadelphia Harisburg AtLocation-1 PPG Delta Logistic RegresssionWeight Feature = Typed Path CityInState, CityInstate-1, CityLocatedInCountry 0.8 0.32 AtLocation-1, AtLocation, CityLocatedInCountry 0.20 Feature Value

  38. [Lao et al, EMNLP 2011] CityLocatedInCountry(Pittsburgh) = ? U.S. Pennsylvania CityLocatedInCountry CityInState-1 CityInState CityInState-1 …(14) Pittsburgh Philadelphia Harisburg Atlanta AtLocation-1 Dallas AtLocation Tokyo PPG Delta Logistic RegresssionWeight Feature = Typed Path CityInState, CityInstate-1, CityLocatedInCountry 0.8 0.32 AtLocation-1, AtLocation, CityLocatedInCountry 0.20 Feature Value

  39. [Lao et al, EMNLP 2011] CityLocatedInCountry(Pittsburgh) = ? U.S. Japan Pennsylvania CityLocatedInCountry CityInState-1 CityInState CityInState-1 …(14) CityLocatedInCountry Pittsburgh Philadelphia Harisburg Atlanta AtLocation-1 Dallas AtLocation Tokyo PPG Delta Logistic RegresssionWeight Feature = Typed Path CityInState, CityInstate-1, CityLocatedInCountry 0.8 0.32 AtLocation-1, AtLocation, CityLocatedInCountry 0.6 0.20 Feature Value

  40. [Lao et al, EMNLP 2011] CityLocatedInCountry(Pittsburgh) = ? U.S. Japan Pennsylvania CityLocatedInCountry CityInState-1 CityInState CityInState-1 …(14) CityLocatedInCountry Pittsburgh Philadelphia Harisburg Atlanta AtLocation-1 Dallas AtLocation Tokyo PPG Delta Logistic RegresssionWeight Feature = Typed Path CityInState, CityInstate-1, CityLocatedInCountry 0.8 0.32 AtLocation-1, AtLocation, CityLocatedInCountry 0.6 0.20 … … … Feature Value CityLocatedInCountry(Pittsburgh) = U.S. p=0.58

  41. Random walk inference: learned path types CityLocatedInCountry(city, country): 8.04 cityliesonriver, cityliesonriver-1, citylocatedincountry 5.42 hasofficeincity-1, hasofficeincity, citylocatedincountry 4.98 cityalsoknownas, cityalsoknownas, citylocatedincountry 2.85 citycapitalofcountry,citylocatedincountry-1,citylocatedincountry 2.29 agentactsinlocation-1, agentactsinlocation, citylocatedincountry 1.22 statehascapital-1, statelocatedincountry 0.66 citycapitalofcountry . . . 7 of the 2985 paths for inferring CityLocatedInCountry

  42. Random Walk Inference: Example NELL/PRA ranking Rank 17 companies by probability competesWith(MSFT,X):

  43. Random Walk Inference: Example NELL/PRA ranking Human Ranking (9 subjs) Rank 17 companies by probability competesWith(MSFT,X): Tractable (bounded length) Anytime Accuracy increases as KB grows Addresses question of how to combine probabilities from different horn clauses demo

  44. Random walk inference: learned path types KB graph augmented by Subj-Verb-Objcorpus statistics CompetesWith(company, company): 5.29 companyAlsoKnownAs, competesWith 2.12 companyAlsoKnownAs, producesProduct, agentInvolvedWith-1 0.77 companyAlsoKnownAs, subj_offer_obj, subj_offer_obj-1 0.65 companyEconomicSector, companyEconomicSector-1 - 0.19 companyAlsoKnownAs - 0.38 companyAlsoKnownAs, companyAlsoKnownAs . . . 6 of the 7966 path types learned for CompetesWith

  45. Summary Key ideas: • Coupled semi-supervised learning • Learn new coupling constraints (Horn clauses) • Automatically extend ontology • Learn progressively more challenging types of K • Scalable random walk probabilistic inference • Integrating symbolic extracted beliefs, + subsymbolic corpus statistics

  46. thank youand thanks to:Darpa, Google, NSF, Intel, Yahoo!, Microsoft, Fullbright

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