1 / 31

Understanding Finite State Automata and Knowledge Representation in Linguistics

This week’s focus explores finite state automata (FSA) as a model for word structure and their role in linguistic knowledge representation. We dive into deterministic and non-deterministic FSAs, their structural components, and transitions, including ε-transitions. Additionally, we examine formal languages, the impact of knowledge representation, inheritance hierarchies, and default inheritance in modeling linguistic facts. DATR, a language for lexical knowledge representation, is introduced, emphasizing its relevance in morphological parsing and the relationship between surface and lexical levels.

keelty
Download Presentation

Understanding Finite State Automata and Knowledge Representation in Linguistics

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Computational language: week 9 • Finish finite state machines • FSA’s for modelling word structure • Declarative language models • knowledge representation and inheritance • non-monotonic or ‘default’ inheritance and lexical knowledge representation • multiple inheritance • DATR: a lexical knowledge representation language

  2. Finite State Automata • Deterministic FSAs • An FSA whose recognition behaviour is fully determined by the state it is in and the input symbol it is looking at

  3. Finite State Automata • Deterministic FSAs • An FSA whose recognition behaviour is fully determined by the state it is in and the input symbol it is looking at • Non-deterministic FSAs • An FSA with decision points

  4. Finite State Automata • Deterministic FSAs • Non-deterministic FSAs • An FSA with decision points • Self-loop may be in a particular state • Arcs may have εtransitions

  5. Finite State Automata • Formal language • Set of strings • Finite symbol set, alphabet

  6. Finite State Automata • Formal language • Set of strings • Finite symbol set, alphabet • L(m) = {baa!, ba!, baaa!,…} “formal language characterised by m” • m = model • L = formal language

  7. Finite State Automata • Formal language • Set of strings • Finite symbol set, alphabet • L(m) = {baa!, ba!, baaa!,…} • A formal language models a fragment of a natural language

  8. Finite state transducers and word structure • FSA for word structure • Handles the morphotactics • Arc labels drawn from sets of lexical classes • Lexical entries and affixes in the lexicon • Achieve morphological recognition

  9. Finite state transducers and word structure • Finite State Transducers for morphological parsing • distinction between surface level and lexical level • lexical level includes basic stem and morphosyntactic features cat +N+PL -> cats

  10. Finite state transducers and word structure • Finite State Transducers for morphological parsing • distinction between surface level and lexical level • lexical level includes basic stem and morphosyntactic features cat +N+PL -> cats • FST: maps one of set of symbols onto another

  11. Finite state transducers and word structure • Finite State Transducers for morphological parsing • distinction between surface level and lexical level • lexical level includes basic stem and morphosyntactic features cat +N+PL -> cats • FST: maps one of set of symbols onto another • c:c a:a t:t +N:εPL:s

  12. Finite state transducers and word structure • Finite State Transducers for morphological parsing • Boundary markers: • ^ morpheme boundary • # word boundary

  13. Finite state transducers and word structure • Finite State Transducers for morphological parsing • Boundary markers: • ^ morpheme boundary • # word boundary • c:c a:a t:t +N:εPL:^s#

  14. Finite state transducers and word structure • Finite State Transducers for morphological parsing • Boundary markers: • ^ morpheme boundary • # word boundary • c:c a:a t:t +N:εPL:^s# • g:g o:e o:e s:s e:e +N:εPL:#

  15. Knowledge representation • Linguistic knowledge • Linguistic facts • Relations between linguistic facts • Computable

  16. Knowledge representation • Linguistic knowledge • Linguistic facts • lexicon • Relations between linguistic facts • Computable

  17. Knowledge representation • Linguistic knowledge • Linguistic facts • lexicon • Relations between linguistic facts • inference • default inference • Computable

  18. Knowledge representation • Linguistic knowledge • Linguistic facts • lexicon • Relations between linguistic facts • inference • default inference • Computable • DATR • Prolog

  19. Knowledge representation and the lexicon • Inheritance hierarchy

  20. Knowledge representation and the lexicon • Inheritance hierarchy • A graphical representation of how facts or properties are shared amongst entities

  21. Knowledge representation and the lexicon • Inheritance hierarchy

  22. Knowledge representation and the lexicon • Inheritance hierarchy

  23. Knowledge representation and the lexicon • Inheritance hierarchy

  24. Knowledge representation and the lexicon • Default inheritance hierarchy

  25. Knowledge representation and the lexicon • Default inheritance

  26. Knowledge representation and the lexicon • Default inheritance

  27. Knowledge representation and the lexicon • Default inheritance

  28. Knowledge representation and the lexicon • Default inheritance

  29. Knowledge representation and the lexicon • DATR, lexical knowledge representation language • Inference and default inference • Compiled in Prolog • Developed by Evans and Gazdar • Network Morphology

  30. Knowledge representation and the lexicon • DATR demonstration

  31. Lexical knowledge representation Summary • Inheritance • Inference rules • Formalism • Computable

More Related