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Morphology

Morphology. What is morphology? Finite State Transducers Two Level Morphology. What is morphology?. Decomposition of words into meaningful units: anti dis establish ment arian ism Interacts with- syntax( categories and word order)

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Morphology

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  1. Morphology What is morphology? Finite State Transducers Two Level Morphology

  2. What is morphology? • Decomposition of words into meaningful units: • anti dis establish ment arian ism • Interacts with- syntax( categories and word order) • [establish] = verb + ment = noun • phonology: divine divinity • obscene obscenity • Interacts with semantics: • boy boys • Peter Peterchen

  3. Phonological String morphological analyzer dictionary lookup syntactic analyzer lexical- semantic analysis discourse processing

  4. Why store all words as morphemes rather than all Morphological combinations as words? What does the morphological analyzer have to output?

  5. The what and the how: • Efficient and effective algorithm to decompose categories into, • or build categories from, component morphemes. • What this algorithm will be depends on problems it has to solve. • In turn depends on representations computed. • Given stem /lemma ( e.g. ‘jump’ add material to change category • Or grammatical properties of word ‘jumped’, ‘jumpable’ • order of composition matters: • ride/ riding • enoble/ enobling/*nobling Adj ---> V, V===> V+ing • trance/*trancing/entrance/entrancing

  6. CONCATENATIVE MORPHOLOGICAL PROCESSES: COMPOUNDING: firefighter PREFIXATION: Un+ well INFIXATION: ( TAGALOG) fikas - strong fumikas - be strong SUFFIXATION: Kick + er CIRCUMFIXATION: ( German) ge [sag] t past prefix [say] past suffix

  7. Inflectional Morphology • non category changing, required by syntax • Agreement: person/number: • Je parle • Nous parlons • Ils parlent • Gender: • la petite ( the little one (fem)) • le petit ( the little one (masc)) • la squelette ( the skeleton)

  8. Derivational Morphology • changes category. Not required by syntax • Deverbal Nominal: • bak+er tion: destroy/destruction • catch+ er Roman's destruction of the city • 'er' = agent of action Catcher of the ball • John’s catcher of the ball • 'John" ~= one who caught

  9. Regular vs Irregular Jump/jumped hit/hit bring/brought sing/sang Productive/Non-Productive adore/adorable, kick/kickable, fax/faxable produce/production destroy/destruction *graft/graftuction Bring/ brought

  10. Regular (English) Verbs

  11. Irregular (English) Verbs

  12. “To love” in Spanish

  13. Productive and rule governed: • fax fax +er • ??? Crudoy cruduction • Category sensitivity: • breakable/* manable • sensitivity/ *hittivity • Semantic sensitivity: • un + well un + happy • *un + ill *un+ sad

  14. Store morphemes or words? lebensversicherungsgesellschaftsangesteller leben+ versicherung + gesellschaft+s+angesteller life insurance company +Poss employee Turkish: Turkish verns have 40k forms

  15. Non- concatenative Morphology • Templatic morphology (Semitic languages):lmd (learn), lamad (he studied), limed (he taught), lumad (he was taught)

  16. Concatenation: Beads on a string Agglutinative ( concatenative) languages are well behaved for FSAs as long as we don’t include phonological or spelling changes Verb Lexicon: jump+edjump kiss+ed kiss stream+ed stream *hopp+ed hop, ??? verb ed q q 1 q q1 q2 0

  17. Pieces of a Morphological Analyzer -er,est,ly un adj-root q2 q3 q0 q1 The lexicon stores the lemmas, and divides them into adjective classes really/clearly *bigly/redly Morphotactics: State sequence indicates order of morpheme composition e.g. comparative or adverb formation is by suffixation

  18. Lexicon • Arranged as TRIE ( letter strings in common relative to position • n-k-e-y • D-o • -g • Classed by part of speech category ( noun, verb) and morphotactic • (which other affixes can precede or follow) • or orthographic considerations.

  19. Orthography • spelling rules- handle phonological or spelling variation in • orthographic a morpheme • Try /trying/tries • Cringe/cringing/cringes

  20. FSA for Inflectional Morphology: English Nouns

  21. FSA for Inflectional Morphology: English Verbs

  22. FSA for Derivational Morphology: Adjectival Formation

  23. More Complex Derivational Morphology

  24. Using FSAs for Recognition: English Nouns and their Inflection

  25. Orthographic • Want association between morpheme and semantic function • Want association between allographs or allophones of the same • phoneme • Allographs: • city -cities • bake- baking • divine-divinity • try tried

  26. Finite State Transducers (FSTs)- the Big Idea Need to relate lexical level, the level that gives us the morphological analysis (+plural,+able to the surface level that keeps track of phonological/ or graphological (spelling_ changes)

  27. Parsing vs recognition • An FSA can give you the string composition of a morphological sequence, and can tell you whether a given morphological string is or is not in the language. It recognizes the string • An FST parses the string. It tells you the morphological structure associated with the string. Other instances of parsing?

  28. Formal definition • An FST defines a relation between sets of pairs of strings: • It contains at least a lexical level that is a concatenation of morphemes • and a surface level that shows the correct spelling for each • morpheme in a given context • cat/sheep ^ s • e.g. noun (instanciated from lexicon) + plural • E s • cats/sheep

  29. Q= finite set of states q0 to qn finite alphabet of complex symbols (feasible pairs) i:o with one symbol from the input alphabet Q0 = the start state F= set of final states  = (q, i:o) the transition function or matrixbetween states. Takes a state from Q and a complex symbol i:o from and returns a new state. feasible pair: a relation of a symbol on one tape to a symbol on the other tape. e.g. can + [pl:^s]

  30. default pair- the upper tape is the same as the lower tape • same input as output :c*a*t/c:c*a:a*t:t*pl:^s • feasible pairs either stated in lexicon if irregular • g:g*o:e*o:e*s:s*e:e goose:geese • or by an automaton that stipulates correspondence in rule • governed way if the relation is regular. If regular, indicated as • Default paris and usually represented by one symbol. • FSTs are closed under: • inversion: switches i/o labels • composition: union of two transducers • one after the other.

  31. trie: in lexicon, categories arranged by letter one at a time with class at end. Allows parallel search as long as things match e.g. m*e*t*a*l <N> m*e*t*a <root> metal, meta-language

  32. Kimmo-BasedMorphological Parsing • Two-level morphology: lexical level + surface level (Koskenniemi 83) • Finite-state transducers (FST): input-output pair

  33. Four-Fold View of FSTs • As a recognizer • As a generator • As a translator • As a set relater

  34. Terminology for Kimmo • Upper = lexical tape • Lower = surface tape • Characters correspond to pairs, written a:b • If “a=b”, write “a” for shorthand • Two-level lexical entries • # = word boundary • ^ = morpheme boundary • Other = “any feasible pair that is not in this tranducer”

  35. Nominal Inflection FST

  36. Lexical and Intermediate Tapes

  37. Spelling Rules

  38. Notation x s z ^ __ s # e --> e /

  39. Intermediate-to-Surface Transducer

  40. Two-Level Morphology

  41. Sample Run

  42. FSTs and ambiguity Parse Example 1: unionizable Parse Example 2: assess

  43. What to do about Global Ambiguity? • Accept first successful structure • Run parser through all possible paths • Bias the search in some manner

  44. Some Limitations

  45. Stemming • For some applications,don’t need full morphological analysis. • IR- don’t care that e.g ‘logician’ is related to ‘logical’ Just want • to know that if you are interested in articles about ‘logic’ • may want former two classes as well. So just want to ‘get back • to root list. • Relate two forms by having a literal relation rule. E.g • al#---> 0 • Is it useful: in a big document may not be necessary because the • will appear in many forms including form in query

  46. stemming is morphologically impoverished so error driven • - can’t distinguish rules that apply at morpheme boundaries • versus internal to root: • patronization = patron + ize + ation • organization = organize+ ation • But the stemmer will treat these as a single class and derive • “organ” as an underlying root. • -’adverse’/’adversity • ‘universe / university

  47. Psycholinguistics • Is the human lexicon efficient in the way computational lexica • are? • -Stanners et al (1979) :where two words are related inflection- • ally,then root stored and other forms rule derived. Where • there is a derivational relationship, then both forms are stored • paradigm = repetition priming • ‘great, happy, peachy, adorable , round, short, great • small • Repetition priming for ‘turns’ given ‘turning’ but not • ‘select’, ‘selective’

  48. Marslen- Wilson et al (1994): May have priming for • Semantically similar derivationally related words: • permit/permission • * create/creativity • On-line versus long term storage lexicon: • Speech errors: ‘we have screw looses’

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