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# markov logic and deep networks - PowerPoint PPT Presentation

Markov Logic and Deep Networks. Pedro Domingos Dept. of Computer Science & Eng. University of Washington. Weight of formula i. No. of true instances of formula i in x. Markov Logic Networks. Basic idea: Use first-order logic to compactly specify large non-i.i.d. models

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### Markov Logic and Deep Networks

Pedro Domingos

Dept. of Computer Science & Eng.

University of Washington

No. of true instances of formula i in x

Markov Logic Networks

• Basic idea: Use first-order logic tocompactly specify large non-i.i.d. models

• MLN = Set of formulas with weights

• Formula = Feature template (Vars→Objects)

• E.g., HMM:

State(+s,t) ^ State(+s',t+1)

Obs(+o,t) ^ State(+s,t)

• Many algorithms for learning and inference

• Inference: Millions of variables, billions of features

• Learning: Generative, discriminative, max-margin, etc.

• Best-performing solutions in many application areas

• Natural language, robot mapping, social networks,computational biology, activity recognition, etc.

• Open-source software/Web site: Alchemy

• Book: Domingos & Lowd, Markov Logic,Morgan & Claypool, 2009.

alchemy.cs.washington.edu

• Very large scale inference

• Defining architecture of deep networks

• Adding knowledge to deep networks

• Transition to natural language input

• Learning with many levels of hidden variables

• Basic idea: Use small amounts of knowledgeand large amounts of joint inferenceto make up for lack of supervision

• Relational clustering:

• Cluster objects with similar relations to similar objects

• Cluster relations that hold between similar sets of objects

• Coreference resolution: Outperforms supervised approaches on MUC and ACE benchmarks

• Semantic network extraction: Learns thousands of concepts and relations from millions of Web triples

Example:Unsupervised Semantic Parsing

• No supervision or annotation

• Input: Dependency parses of sentences(Nodes→Unary predicates / Edges→Binary preds.)

• Outputs: Semantic parser and knowledge base

• Basic idea: Cluster expressions with similar subexpressions

• Maps syntactic variants to common meaning

• Discovers its own meaning representation

• “Part of” lattice of clusters

• Applied to corpus of biomedical abstracts

• Three times more correct answers than next best