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Morphology: Words and their Parts

Morphology: Words and their Parts

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Morphology: Words and their Parts

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  1. Morphology: Wordsand their Parts CS 4705 Slides adapted from Jurafsky, Martin Hirschberg and Dorr.

  2. English Morphology • Morphology is the study of the ways that words are built up from smaller meaningful units called morphemes • We can usefully divide morphemes into two classes • Stems: The core meaning bearing units • Affixes: Bits and pieces that adhere to stems to change their meanings and grammatical functions

  3. Nouns and Verbs (English) • Nouns are simple (not really) • Markers for plural and possessive • Verbs are only slightly more complex • Markers appropriate to the tense of the verb

  4. Regulars and Irregulars • Ok so it gets a little complicated by the fact that some words misbehave (refuse to follow the rules) • Mouse/mice, goose/geese, ox/oxen • Go/went, fly/flew • The terms regular and irregular will be used to refer to words that follow the rules and those that don’t.

  5. Regular and Irregular Nouns and Verbs • Regulars… • Walk, walks, walking, walked, walked • Table, tables • Irregulars • Eat, eats, eating, ate, eaten • Catch, catches, catching, caught, caught • Cut, cuts, cutting, cut, cut • Goose, geese

  6. Why care about morphology? Spelling correction: referece • Morphology in machine translation • Spanish words quiero and quieres are both related to querer ‘want’ • Hyphenation algorithms: refer-ence • Part-of-speech analysis: google, googler • Text-to-speech: grapheme-to-phoneme conversion • hothouse (/T/ or /D/) • Allows us to guess at meaning • ‘Twas brillig and the slithy toves… • Muggles moogled migwiches

  7. Concatenative Morphology • Morpheme+Morpheme+Morpheme+… • Stems: often called lemma, base form, root, lexeme • hope+ing hoping hop hopping • Affixes • Prefixes: Antidisestablishmentarianism • Suffixes: Antidisestablishmentarianism • Infixes: hingi (borrow) – humingi (borrower) in Tagalog • Circumfixes: sagen (say) – gesagt (said) in German

  8. What useful information does morphology give us? • Different things in different languages • Spanish: hablo, hablaré/ English: I speak, I will speak • English: book, books/ Japanese: hon, hon • Languages differ in how they encode morphological information • Isolating languages (e.g. Cantonese) have no affixes: each word usually has 1 morpheme • Agglutinative languages (e.g. Finnish, Turkish) are composed of prefixes and suffixes added to a stem (like beads on a string) – each feature realized by a single affix, e.g. Finnish

  9. epäjärjestelmällistyttämättömyydellänsäkäänköhän ‘Wonder if he can also ... with his capability of not causing things to be unsystematic’ • Inflectional languages (e.g. English) merge different features into a single affix (e.g. ‘s’ in likes indicates both person and tense); and the same feature can be realized by different affixes • Polysynthetic languages (e.g. Inuit languages) express much of their syntax in their morphology, incorporating a verb’s arguments into the verb, e.g. Western Greenlandic Aliikusersuillammassuaanerartassagaluarpaalli.aliiku-sersu-i-llammas-sua-a-nerar-ta-ssa-galuar-paal-lientertainment-provide-SEMITRANS-one.good.at-COP-say.that-REP-FUT-sure.but-3.PL.SUBJ/3SG.OBJ-but'However, they will say that he is a great entertainer, but ...' • So….different languages may require very different morphological analyzers

  10. What we want • Something to automatically do the following kinds of mappings: • Cats cat +N +PL • Cat cat +N +SG • Cities city +N +PL • Merging merge +V +Present-participle • Caught catch +V +past-participle

  11. Morphology Can Help Define Word Classes • AKA morphological classes, parts-of-speech • Closed vs. open (function vs. content) class words • Pronoun, preposition, conjunction, determiner,… • Noun, verb, adverb, adjective,… • Identifying word classes is useful for almost any task in NLP, from translation to speech recognition to topic detection…very basic semantics

  12. (English) Inflectional Morphology Word stem + grammatical morpheme  different forms of same word • Usually produces word of same class • Usually serves a syntactic or grammatical function (e.g. agreement) like  likes or liked bird  birds • Nominal morphology • Plural forms • s or es • Irregular forms (goose/geese)

  13. Mass vs. count nouns (fish/fish(es), email or emails?) • Possessives (cat’s, cats’) • Verbal inflection • Main verbs (sleep, like, fear) relatively regular • -s, ing, ed • And productive: emailed, instant-messaged, faxed, homered • But some are not: • eat/ate/eaten, catch/caught/caught • Primary (be, have, do) and modal verbs (can, will, must) often irregular and not productive • Be: am/is/are/were/was/been/being • Irregular verbs few (~250) but frequently occurring

  14. Derivational Morphology • Word stem + syntactic/grammaticalmorpheme  new words • Usually produces word ofdifferent class • Incomplete process: derivational morphs cannot be applied to just any member of a class • Verbs --> nouns • -ize verbs  -ation nouns • generalize, realize  generalization, realization • synthesize but not synthesization

  15. Verbs, nouns  adjectives • embrace, pity embraceable, pitiable • care, wit  careless, witless • Adjective  adverb • happy  happily • Process selective in unpredictable ways • Less productive: nerveless/*evidence-less, malleable/*sleep-able, rar-ity/*rareness • Meanings of derived terms harder to predict by rule • clueless, careless, nerveless, sleepless

  16. Compounding • Two base forms join to form a new word • Bedtime, Weinerschnitzel, Rotwein • Careful? Compound or derivation?

  17. Morphotactics • What are the ‘rules’ for constructing a word in a given language? • Pseudo-intellectual vs. *intellectual-pseudo • Rational-ize vs *ize-rational • Cretin-ous vs. *cretin-ly vs. *cretin-acious

  18. Semantics: In English, un- cannot attach to adjectives that already have a negative connotation: • Unhappy vs. *unsad • Unhealthy vs. *unsick • Unclean vs. *undirty • Phonology: In English, -er cannot attach to words of more than two syllables • great, greater • Happy, happier • Competent, *competenter • Elegant, *eleganter • Unruly, ?unrulier

  19. Morphological Parsing • These regularities enable us to create software to parse words into their component parts

  20. Morphology and FSAs • We’d like to use the machinery provided by FSAs to capture facts about morphology • Ie. Accept strings that are in the language • And reject strings that are not • And do it in a way that doesn’t require us to in effect list all the words in the language

  21. What do we need to build a morphological parser? • Lexicon: list of stems and affixes (w/ corresponding p.o.s.) • Morphotactics of the language: model of how and which morphemes can be affixed to a stem • Orthographic rules: spelling modifications that may occur when affixation occurs • in  il in context of l (in- + legal) • Most morphological phenomena can be described with regular expressions – so finite state techniques often used to represent morphological processes

  22. Start Simple • Regular singular nouns are ok • Regular plural nouns have an -s on the end • Irregulars are ok as is

  23. Simple Rules

  24. Now Add in the Words

  25. q1 q2 q0 adj-root1 -er, -ly, -est un- • Derivational morphology: adjective fragment adj-root1 q5 q3 q4  -er, -est adj-root2 • Adj-root1: clear, happi, real (clearly) • Adj-root2: big, red (*bigly)

  26. Parsing/Generation vs. Recognition • We can now run strings through these machines to recognize strings in the language • Accept words that are ok • Reject words that are not • But recognition is usually not quite what we need • Often if we find some string in the language we might like 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”

  27. 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”

  28. Applications • The kind of parsing we’re talking about is normally called morphological analysis • It can either be • An important stand-alone component of an application (spelling correction, information retrieval) • Or simply a link in a chain of processing

  29. FSTs • Kimmo Koskenniemi’s two-level morphology • Idea: word is a relationship betweenlexicallevel (its morphemes) and surface level (its orthography)

  30. +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

  31. 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. • In general, FSTs can be used for • Translators (Hello:Ciao) • Parser/generators (Hello:How may I help you?) • As well as Kimmo-style morphological parsing

  32. 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

  33. Ambiguity • What’s the right parse (segmentation) for • Unionizable • Union-ize-able • Un-ion-ize-able • Each represents a valid path through the derivational morphology machine.

  34. 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

  35. 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

  36. 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

  37. 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

  38. 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

  39. Lexical to Intermediate Level

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

  41. Foxes

  42. 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.

  43. 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

  44. Overall Scheme

  45. Cascades • This is a scheme that we’ll see again and again. • Overall processing is divided up into distinct rewrite steps • The output of one layer serves as the input to the next • The intermediate tapes may or may not wind up being useful in their own right

  46. Porter Stemmer (1980) • Used for tasks in which you only care about the stem • IR, modeling given/new distinction, topic detection, document similarity • Lexicon-free morphological analysis • Cascades rewrite rules (e.g. misunderstanding --> misunderstand --> understand --> …) • Easily implemented as an FST with rules e.g. • ATIONAL  ATE • ING  ε • Not perfect …. • Doing doe

  47. Policy police • Does stemming help? • IR, little • Topic detection, more

  48. Summing Up • FSTs provide a useful tool for implementing a standard model of morphological analysis, Kimmo’s two-level morphology • But for many tasks (e.g. IR) much simpler approaches are still widely used, e.g. the rule-based Porter Stemmer • Next time: • Read Ch 4 • HW1 assigned; see web page: http://www.cs.columbia.edu/~kathy/NLP