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FST Morphology

FST Morphology. Miriam Butt October 2003. Based on Beesley and Karttunen 2003. Recap. Last Time: Finite State Automata can model most anything that involes a finite amount of states. We modeled a Coke Machine and saw that it could also be thought of as defining a language.

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FST Morphology

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  1. FST Morphology Miriam Butt October 2003 Based on Beesley and Karttunen 2003

  2. Recap Last Time: Finite State Automata can model most anything that involes a finite amount of states. We modeled a Coke Machine and saw that it could also be thought of as defining a language. We will now look at the extension to natural language more closely.

  3. A One-Word Language c a n t o

  4. A Three-Word Language i g r e t c a n t o m a e s

  5. Analysis: A Successful Match i g r e t c a n t o m a e s Input: m e s a

  6. Rejects The analysis of libro, tigra, cant, mesas will fail. Why? Use: for example for a spell checker

  7. Transducers: Beyond Accept and Reject i g r e t i g r e t c a n t o c a n t o m a e s m a e s

  8. Transducers: Beyond Accept and Reject Analysis Process: • Start at the Start State • Match the input symbols of string against the lower-side symbol on the arcs, consuming the input symbols and finding a path to a final state. • If successful, return the string of upper-side symbols on the path as the result. • If unsucessful, return nothing.

  9. A Two-Level Transducer i g r e t i g r e t c a n t o c a n t o m a e s m a e s Input: m e s a Output: m e s a

  10. A Lexical Transducer +PresInd +Sg +Verb c a n t a r +1P c a n t 0 0 0 0 o 0 Input: c a n t o Output: c a n t a r +Verb +PresInd +1P +Sg One Possible Path through the Network

  11. A Lexical Transducer c a n t o +Sg +Noun +Masc c a n t o 0 0 0 Input: c a n t o Output: c a n t o +Noun +Masc +Sg Another Possible Path through the Network (found through backtracking)

  12. Why Transducer? General Definition: Device that converts energy from one form to another. In this Context: Device that converts one string of symbols into another another string of symbols.

  13. The Tags Tags or Symbols like +Noun or +Verb are arbitrary: the naming convention is determined by the (computational) linguist and depends on the larger picture (type of theory/type of application). One very successful tagging/naming convention is the Penn Treebank Tag Set

  14. The Tags What kind of Tags might be useful?

  15. Generation vs. Analysis The same finite state transducers we have been using for the analysis of a given surface string can also be used in reverse: for generation. The XRCE people think of analysis as lookup, of generation of lookdown.

  16. Generation --- Lookdown +PresInd +Sg +Verb c a n t a r +1P c a n t 0 0 0 0 o 0 Input: c a n t a r +Verb +PresInd +1P +Sg Output: c a n t o

  17. Generation --- Lookdown Analysis Process: • Start at the Start State and the beginning of the input string • Match the input symbols of string against the upper-side symbols on the arcs, consuming the input symbols and finding a path to a final state. • If successful, return the string of lower-side symbols on the path as the result. • If generation is unsuccessful, return nothing.

  18. Tokenization General String Conversion: Tokenizer Task: Divide up running text into individual tokens • couldn’t -> could not • to and from -> to-and-fro • ,,, -> , • The farmer walked -> The^TBfarmer^TBwalked

  19. Sharing Structure and Sets Networks can be compressed quite cleverly (p. 16-17). Sets: FST Networks are based on formal language theory. This includes basics of set theory: membership, union, intersection, subtraction, complementation

  20. Some Basic Concepts/Representations Empty Language Empty-String Language

  21. a Some Basic Concepts/Representations A Network for an Infinite Language ? A Network for the Universal Language

  22. Relations Ordered Set: members are ordered. Ordered Pair: <A,B> vs. <B,A> • Relation: set whose members are ordered pairs • Family Trees (p. 21) • { <“cantar+Verb+PresInd+1P+Sg”, “canto”>, • <“canto+Noun+Masc+Sg”, “canto”>, • ... }

  23. A B a C b ... c Z z Relations An Infinite Relation:. Identity Relation: <“canto”, “canto”>

  24. Basic Set Operations • union(p. 24) • intersection (p. 25) • subtraction (p. 25)

  25. Concatenation One can also concatenate two existing languages (finite state networks with one another to build up new words productively/dynamically). This works nicely, but one has to write extra rules to avoid things like: *trys, *tryed, though trying is okay.

  26. Concatenation w o r k Network for the Language {“work”} s n g i d e Network for the Language {“s”, “ed”, “ing”}

  27. Concatenation s k w o r g n i e d Concatenation of the two networks What strings/language does this result in?

  28. Composition Composition is an operation on two relations. Composition of the two relations <x,y> and <y,z> yields <x, z> Example: <“cat”, “chat”> with <“chat”, “Katze”> gives <“cat”, “Katze”>

  29. Composition c a t c h a t c h a t K a t z e

  30. Composition c a t c h a t K a t z e Merging the two networks

  31. Composition c a t K a t z e The Composition of the Networks What is this reminiscent of?

  32. Composition + Rule Application Composition can be used to encode phonology-style rules. E.g., Lexical Phonology assumes several iterations/levels at which different kinds of rules apply. This can be modeled in terms of cascades of FST transducers (p. 35). These can then be composed together into one single transducer.

  33. Next Time • Begin working with xfst • Now: Exercises 1.10.1, 1.10.2, 1.10.4 • Optional: 1.10.3

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