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CS60057 Speech &Natural Language Processing

CS60057 Speech &Natural Language Processing. Autumn 2007. Lecture4 1 August 2007. MORPHOLOGY. Finite State Machines. FSAs are equivalent to regular languages FSTs are equivalent to regular relations (over pairs of regular languages) FSTs are like FSAs but with complex labels.

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CS60057 Speech &Natural Language Processing

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  1. CS60057Speech &Natural Language Processing Autumn 2007 Lecture4 1 August 2007 Natural Language Processing

  2. MORPHOLOGY Natural Language Processing

  3. Finite State Machines • FSAs are equivalent to regular languages • FSTs are equivalent to regular relations (over pairs of regular languages) • FSTs are like FSAs but with complex labels. • We can use FSTs to transduce between surface and lexical levels. Natural Language Processing

  4. Simple Rules Natural Language Processing

  5. Adding in the Words Natural Language Processing

  6. Derivational Rules Natural Language Processing

  7. Parsing/Generation vs. Recognition • Recognition is usually not quite what we need. • Usually if we find some string in the language we need to find the structure in it (parsing) • Or we have some structure and we want to produce a surface form (production/generation) • Example • From “cats” to “cat +N +PL”and back Natural Language Processing

  8. Morphological Parsing • Given the input cats, we’d like to outputcat +N +Pl, telling us that cat is a plural noun. • Given the Spanish input bebo, we’d like to outputbeber +V +PInd +1P +Sg telling us that bebo is the present indicative first person singular form of the Spanish verb beber, ‘to drink’. Natural Language Processing

  9. Morphological Anlayser To build a morphological analyser we need: • lexicon: the list of stems and affixes, together with basic information about them • morphotactics: the model of morpheme ordering (eg English plural morpheme follows the noun rather than a verb) • orthographic rules: these spelling rules are used to model the changes that occur in a word, usually when two morphemes combine (e.g., fly+s = flies) Natural Language Processing

  10. Lexicon & Morphotactics • Typically list of word parts (lexicon) and the models of ordering can be combined together into an FSA which will recognise the all the valid word forms. • For this to be possible the word parts must first be classified into sublexicons. • The FSA defines the morphotactics (ordering constraints). Natural Language Processing

  11. Sublexiconsto classify the list of word parts Natural Language Processing

  12. FSA Expresses Morphotactics (ordering model) Natural Language Processing

  13. Towards the Analyser • We can use lexc or xfst to build such an FSA • To augment this to produce an analysis we must create a transducer Tnum which maps between the lexical level and an "intermediate" level that is needed to handle the spelling rules of English. Natural Language Processing

  14. Three Levels of Analysis Natural Language Processing

  15. 1. Tnum: Noun Number Inflection • multi-character symbols • morpheme boundary ^ • word boundary # Natural Language Processing

  16. Intermediate Form to Surface • The reason we need to have an intermediate form is that funny things happen at morpheme boundaries, e.g. cat^s  cats fox^s  foxes fly^s  flies • The rules which describe these changes are called orthographic rules or "spelling rules". Natural Language Processing

  17. More English Spelling Rules • consonant doubling: beg / begging • y replacement: try/tries • k insertion: panic/panicked • e deletion: make/making • e insertion: watch/watches • Each rule can be stated in more detail ... Natural Language Processing

  18. Spelling Rules • Chomsky & Halle (1968) invented a special notation for spelling rules. • A very similar notation is embodied in the "conditional replacement" rules of xfst. E -> F || L _ R which means replace E with F when it appears between left context L and right context R Natural Language Processing

  19. A Particular Spelling Rule This rule does e-insertion ^ -> e || x _ s# Natural Language Processing

  20. e insertion over 3 levels The rule corresponds to the mapping between surface and intermediate levels Natural Language Processing

  21. e insertion as an FST Natural Language Processing

  22. Incorporating Spelling Rules • Spelling rules, each corresponding to an FST, can be run in parallel provided that they are "aligned". • The set of spelling rules is positioned between the surface level and the intermediate level. • Parallel execution of FSTs can be carried out: • by simulation: in this case FSTs must first be aligned. • by first constructing a a single FST corresponding to their intersection. Natural Language Processing

  23. Putting it all together execution of FSTi takes place in parallel Natural Language Processing

  24. Kaplan and Kay: The Xerox View FSTi are aligned but separate FSTi intersected together Natural Language Processing

  25. Finite State Transducers • The simple story • Add another tape • Add extra symbols to the transitions • On one tape we read “cats”, on the other we write “cat +N +PL”, or the other way around. Natural Language Processing

  26. FSTs Natural Language Processing

  27. English Plural Natural Language Processing

  28. +N:ε +PL:s c:c a:a t:t Transitions • c:c means read a c on one tape and write a c on the other • +N:ε means read a +N symbol on one tape and write nothing on the other • +PL:s means read +PL and write an s Natural Language Processing

  29. Typical Uses • Typically, we’ll read from one tape using the first symbol on the machine transitions (just as in a simple FSA). • And we’ll write to the second tape using the other symbols on the transitions. Natural Language Processing

  30. Ambiguity • Recall that in non-deterministic recognition multiple paths through a machine may lead to an accept state. • Didn’t matter which path was actually traversed • In FSTs the path to an accept state does matter since differ paths represent different parses and different outputs will result Natural Language Processing

  31. Ambiguity • What’s the right parse for • Unionizable • Union-ize-able • Un-ion-ize-able • Each represents a valid path through the derivational morphology machine. Natural Language Processing

  32. Ambiguity • There are a number of ways to deal with this problem • Simply take the first output found • Find all the possible outputs (all paths) and return them all (without choosing) • Bias the search so that only one or a few likely paths are explored Natural Language Processing

  33. The Gory Details • Of course, its not as easy as • “cat +N +PL” <-> “cats” • As we saw earlier there are geese, mice and oxen • But there are also a whole host of spelling/pronunciation changes that go along with inflectional changes • Cats vs Dogs • Fox and Foxes Natural Language Processing

  34. Multi-Tape Machines • To deal with this we can simply add more tapes and use the output of one tape machine as the input to the next • So to handle irregular spelling changes we’ll add intermediate tapes with intermediate symbols Natural Language Processing

  35. Generativity • Nothing really privileged about the directions. • We can write from one and read from the other or vice-versa. • One way is generation, the other way is analysis Natural Language Processing

  36. Multi-Level Tape Machines • We use one machine to transduce between the lexical and the intermediate level, and another to handle the spelling changes to the surface tape Natural Language Processing

  37. Lexical to Intermediate Level Natural Language Processing

  38. Intermediate to Surface • The add an “e” rule as in fox^s# <-> foxes# Natural Language Processing

  39. Foxes Natural Language Processing

  40. Note • A key feature of this machine is that it doesn’t do anything to inputs to which it doesn’t apply. • Meaning that they are written out unchanged to the output tape. • Turns out the multiple tapes aren’t really needed; they can be compiled away. Natural Language Processing

  41. Overall Scheme • We now have one FST that has explicit information about the lexicon (actual words, their spelling, facts about word classes and regularity). • Lexical level to intermediate forms • We have a larger set of machines that capture orthographic/spelling rules. • Intermediate forms to surface forms Natural Language Processing

  42. Overall Scheme Natural Language Processing

  43. http://nltk.sourceforge.net/index.php/Documentation Natural Language Processing

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