NEVER-ENDING LANGUAGE LEARNER Student: Nguyễn Hữu Thành Phạm Xuân Khoái Vũ Mạnh Cầm Instructor: PhD Lê Hồng Phương Hà Nội, January

Idea: Build a structuring KB. What is KB?  Categories: cities, companies, sport teams….  Relations: hasOfficeIn(organisation, location)  Noun Phrase What is structuring KB?

Globe and Mail Stanley Cup hockey NHL Toronto CFRB Wilson play hired won Maple Leafs home town city paper league Sundin Milson writer radio Maple Leaf Gardens team stadium Canada city stadium politician country Miller airport member Toskala Pearson Skydome Connaught Sunnybrook hospital city company skateshelmet uses equipment won Red Wings Detroi t hometown GM city company competes with Toyota plays in league Prius Corrola created Hino acquired automobile economic sector city stadium Idea: Structuring Knowledge Base climbing football uses equipment

Ideas: using Machine Learning Machine Learning: a branch of artificial intelligence, concerns the construction and study of systems that can learn from data.artificial intelligencelearn

Ideas Seed examples Web NELL Knowledge Base (KB) Human trainers Initial ontology

Ideas: the task run 24x7, forever each day: 1.Reading task: extract more facts from the web to populate the initial ontology. 2.Learning task: learn to read (perform #1) better than yesterday.

NELL Architecture Beliefs Candidate facts Knowledge Integrator CPL RL CMC CSEAL Data Resources Knowledge Base Subsystem Components

Coupled Pattern Learner (CPL) - Learns to extract category and relation instances/ pattern from unstructure text. - Learns contextual pattern that high-precision extractor for each predicate. - Eg: + Trang An la ten mot co gai. + Trang An la ten mot cong ty.  Use it to improve high-precision

Input/Output - Input : + Larger text corpus + Initial ontology containing the information. - Output: + Proposed instances/ contextual pattern for each predicate.

Input: An ontology O, and a text corpus C Output: Trusted instances/patterns for each predicate for i=1,2,...,∞ do foreach predicate p in O do EXTRACT candidate instances/contextual patterns using recently promoted patterns/instances; FILTER candidates that violate coupling; RANK candidate instances/patterns; PROMOTE top candidates; end

Example: Samsung v ừ a tung clip ch ế nh ạ o s ả n ph ẩ m m ớ i c ủ a Nokia. Example: Samsung v ừ a tung clip ch ế nh ạ o s ả n ph ẩ m m ớ i c ủ a Nokia. CityHa Noi, Ho Chi Minh, Da Nang,... CompanySon Ha, Kinh Do,... competesWith(AMD, Intel), (Google, Microsoft), (Samsung, Nokia),...

Coupled SEAL Beliefs CSEAL New candidate facts Internet

Coupled SEAL SEAL ( Set Expander for Any Language ): expands entities automatically by utilizing resources from the Web CSEAL adds mutual-exclusion and type- checking constraints

Coupled SEAL Coupled SEAL :: A semi-structured extractor Queries the internet with sets of beliefs from each category or relation; mines lists and tables for instances Uses mutual exclusion relationships to provide negative examples for filtering overly general lists and tables 5 queries/category 10 queries/relation fetches 50 web pages/query probabilities assigned as in CPL

Coupled SEAL Example:

Coupled Morphological Classifier KB Data Resources CMC New candidate facts CMC classify NP based on various morphological features (words, capitalization, affixes)

Coupled Morphological Classifier Ex1: Bach Mai hotel  hotel(Bach Mai) Ex2: Mai  person(Mai) Ex3: tradition  noun(tradition)

Coupled Morphological Classifier Beliefs from KB are used as training instances CMC examines candidate facts proposed by other components and classifies up to 30 new beliefs/candidate

Rule Learner Candidate facts Beliefs RL New candidate facts RL uses categories and relations in KB as its input and make new relations for KB.

Rule Learner Example 1: playSport(Rooney, football)  athlete(Rooney), sport(football) Example2: isCapital(Hanoi, Vietnam), liveIn(Thanh, Hanoi), roommate(Thanh, Khoai), roommate(Khoai, Cam)  liveIn(Thanh, Vietnam), roommate(Thanh, Cam), liveIn(Khoai, Hanoi)…..

Rule Learner Some kinds of Rule Learner Systems: OneR, Ridor, PART, JRip, ConjunctiveRule. Clip: tDeu2ic

Initial result Running 24x7, since January, 12, 2010 Inputs: ontology defining >600 categories and relations seed examples of each 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

Initial result Demo: erage:beer erage:beer

References NELL article: aaai10.pdf guage_learning/ guage_learning/ Tom Mitchell’s seminar: RL: learner-or-rule-induction/