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Finite-State Automata

Finite-State Automata. Shallow Processing Techniques for NLP Ling570 October 5, 2011. Roadmap. Finite-state automata variants Non-deterministic Finite-State Automata (NFA) Probabilistic Finite-State Automata (PFSA) Finite-State Transducers (FST) Intro CARMEL. FSAs Formally.

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Finite-State Automata

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  1. Finite-State Automata Shallow Processing Techniques for NLP Ling570 October 5, 2011

  2. Roadmap • Finite-state automata variants • Non-deterministic Finite-State Automata (NFA) • Probabilistic Finite-State Automata (PFSA) • Finite-State Transducers (FST) • Intro • CARMEL

  3. FSAs Formally • A Finite-State Automaton (FSA) is a 5-tuple: • A set of states Q {q0,q1,q2,q3,q4} • A finite alphabet Σ {b,a,!} • A state state q0 • A set of accepting states {q4} • A transition function Q x Σ Q

  4. Non-deterministic Finite-State Automata • Deterministic FSA: • Given current state x input, at most one legal transition

  5. Non-deterministic Finite-State Automata • Deterministic FSA: • Given current state x input, at most one legal transition • Sources of non-determinism in NFA

  6. Non-deterministic Finite-State Automata • Deterministic FSA: • Given current state x input, at most one legal transition • Sources of non-determinism in NFA • More than one transition for qi x a

  7. Non-deterministic Finite-State Automata • Deterministic FSA: • Given current state x input, at most one legal transition • Sources of non-determinism in NFA • More than one transition for qi x a • ε-transitions • Transitions that consume no input

  8. Non-deterministic Finite-State Automata • Deterministic FSA: • Given current state x input, at most one legal transition • Sources of non-determinism in NFA • More than one transition for qi x a • ε-transitions • Transitions that consume no input • Multiple start states • Transitions: • Q x (ΣU ε)  2S

  9. Sheeptalk NFA • Multiple choice:

  10. Sheeptalk NFA • Multiple choice: • ε-transitions

  11. NFA & DFA • What is the relationship b/t NFAs and DFAs?

  12. NFA & DFA • What is the relationship b/t NFAs and DFAs? • NFAs and DFAs are equivalent • Accept same languages

  13. NFA & DFA • What is the relationship b/t NFAs and DFAs? • NFAs and DFAs are equivalent • Accept same languages • Standard transformation from NFA to DFA: • Create new state for each equivalence class in NFA

  14. NFA & DFA • What is the relationship b/t NFAs and DFAs? • NFAs and DFAs are equivalent • Accept same languages • Standard transformation from NFA to DFA: • Create new state for each equivalence class in NFA • If NFA has N states, up to 2N states in DFA

  15. NFA & DFA • What is the relationship b/t NFAs and DFAs? • NFAs and DFAs are equivalent • Accept same languages • Standard transformation from NFA to DFA: • Create new state for each equivalence class in NFA • If NFA has N states, up to 2N states in DFA • Why use both?

  16. Managing Non-Determinism • Approaches to non-determinism

  17. Managing Non-Determinism • Approaches to non-determinism • Backup/backtracking: • At choice points, mark state, input • If fail, return and try alternative

  18. Managing Non-Determinism • Approaches to non-determinism • Backup/backtracking: • At choice points, mark state, input • If fail, return and try alternative • Look-ahead: • Look ahead to see which choice to take

  19. Managing Non-Determinism • Approaches to non-determinism • Backup/backtracking: • At choice points, mark state, input • If fail, return and try alternative • Look-ahead: • Look ahead to see which choice to take • Parallelism: • At choice points, consider all paths in parallel • Basically the NFA  DFA conversion process

  20. Recognition in NFAs • Two standard approaches:

  21. Recognition in NFAs • Two standard approaches: • Transformation: • Convert NFA  DFA • Perform standard DFA recognition

  22. Recognition in NFAs • Two standard approaches: • Transformation: • Convert NFA  DFA • Perform standard DFA recognition • Search: • Explicitly model recognition as state-space search • Perform backtracking as necessary

  23. Recognition in NFA • For strings in the language:

  24. Recognition in NFA • For strings in the language: • There exists some state sequence leading to a final state

  25. Recognition in NFA • For strings in the language: • There exists some state sequence leading to a final state • Not all paths necessarily lead to an accept state • For strings not in the language:

  26. Recognition in NFA • For strings in the language: • There exists some state sequence leading to a final state • Not all paths necessarily lead to an accept state • For strings not in the language: • No paths lead to an accept state

  27. NFA Recognition as Search • Search problem:

  28. NFA Recognition as Search • Search problem: • Start search-state • (start node, beginning of input)

  29. NFA Recognition as Search • Search problem: • Start search-state • (start node, beginning of input) • Goal test • (final node, end of input)

  30. NFA Recognition as Search • Search problem: • Start search-state • (start node, beginning of input) • Goal test • (final node, end of input) • Successor function • Transitions

  31. NFA Recognition as Search • Search problem: • Start search-state • (start node, beginning of input) • Goal test • (final node, end of input) • Successor function • Transitions • Path cost

  32. NFA Recognition as Search • Search problem: • Start search-state • (start node, beginning of input) • Goal test • (final node, end of input) • Successor function • Transitions • Path cost • Use standard search algorithms • e.g. Depth-first, breadth first, A*

  33. Example

  34. Example

  35. Example

  36. Example

  37. Example

  38. Example

  39. Example

  40. Example

  41. Breadth-first Search

  42. Equivalences and Operations • FSAs equivalent to Regular Languages (Regex, etc) • Can be shown by induction • Demonstrate primitive regex ops imitated by automata

  43. Equivalences and Operations • FSAs equivalent to Regular Languages (Regex, etc) • Can be shown by induction • Demonstrate primitive regex ops imitated by automata

  44. Equivalences and Operations • FSAs equivalent to Regular Languages (Regex, etc) • Can be shown by induction • Demonstrate primitive regex ops imitated by automata

  45. Equivalences and Operations • FSAs equivalent to Regular Languages (Regex, etc) • Can be shown by induction • Demonstrate primitive regex ops imitated by automata

  46. Equivalences and Operations • FSAs equivalent to Regular Languages (Regex, etc) • Can be shown by induction • Demonstrate primitive regex ops imitated by automata • E.g. Concatenation

  47. Equivalences and Operations • FSAs equivalent to Regular Languages (Regex, etc) • Can be shown by induction • Demonstrate primitive regex ops imitated by automata • E.g. Concatenation

  48. Equivalences and Operations • Closure: • Union:

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