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 Morphological Form ClassesRegularly Inflected Verbs Stemwalkmergetrymap -s formwalksmergestriesmaps -ing formwalkingmergingtryingmapping Past form or –ed participlewalkedmergedtriedmapped

11. Irregular (English) Verbs Morphological Form ClassesIrregularly Inflected Verbs Stemeatcatchcut -s formeatscatchescuts -ing formeatingcatchingcutting Past formatecaughtcut -ed participleeatencaughtcut

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

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+ed jump kiss+ed kiss stream+ed stream *hopp+ed hop, ??? q q 0 verb q 1 ed q2 q1

17. q0q1 q3 q2 un adj-root - er,est,ly 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 Pieces of a Morphological Analyzer

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 q 0 to q n  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 m*e*t*a metal, meta-language

32. Kimmo-Based Morphological 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 NameRule DescriptionExample Consonant Doubling1-letter consonant doubled before -ing/-edbeg/begging E-deletionSilent e dropped before -ing and -edmake/making E-insertione added after s,z,x,ch,sh before swatch/watches Y-replacement-y changes to -ie before -s, -i before -edtry/tries K-insertionverbs ending with vowel + -c add -kpanic/panicked

38. Notation  e / xszxsz ^ __ s #

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

47. 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

48. 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

49. 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’

50. 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’