Morphology: Parsing Words

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Lecture 3

• Morphology Parsing Words

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What is morphology?

• The study of how words are composed from
  smaller, meaning-bearing units (morphemes)
• Stems children, undoubtedly,
• Affixes (prefixes, suffixes, circumfixes,
  infixes)
• Immaterial
• Trying
• Gesagt
• Absobldylutely
• Concatenative vs. non-concatenative (e.g. Arabic
  root-and-pattern) morphological systems

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Morphology Helps 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,

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(English) Inflectional Morphology

• Word stem grammatical morpheme
• Usually produces word of same class
• Usually serves a syntactic function (e.g.
  agreement)
• like ? likes or liked
• bird ? birds
• Nominal morphology
• Plural forms
• s or es
• Irregular forms (goose/geese)
• Mass vs. count nouns (fish/fish,email or emails?)
• Possessives (cats, cats)

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• Verbal inflection
• Main verbs (sleep, like, fear) verbs relatively
  regular
• -s, ing, ed
• And productive Emailed, instant-messaged, faxed,
  homered
• But some are not regular 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
• So.English inflectional morphology is fairly
  easy to model.with some special cases...

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(English) Derivational Morphology

• Word stem grammatical morpheme
• Usually produces word of different class
• More complicated than inflectional
• E.g. verbs --gt nouns
• -ize verbs ? -ation nouns
• generalize, realize ? generalization, realization
• E.g. verbs, nouns ? adjectives
• embrace, pity? embraceable, pitiable
• care, wit ? careless, witless

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• E.g. adjective ? adverb
• happy ? happily
• But rules have many exceptions
• Less productive evidence-less, concern-less,
  go-able, sleep-able
• Meanings of derived terms harder to predict by
  rule
• clueless, careless, nerveless

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Parsing

• Taking a surface input and identifying its
  components and underlying structure
• Morphological parsing parsing a word into stem
  and affixes, identifying its parts and their
  relationships
• Stem and features
• goose ? goose N SG or goose V
• geese ? goose N PL
• gooses ? goose V 3SG
• Bracketing indecipherable ? in de cipher
  able

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Why parse words?

• For spell-checking
• Is muncheble a legal word?
• To identify a words part-of-speech (pos)
• For sentence parsing, for machine translation,
• To identify a words stem
• For information retrieval
• Why not just list all word forms in a lexicon?

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How do people represent words?

• Hypotheses
• Full listing hypothesis words listed
• Minimum redundancy hypothesis morphemes listed
• Experimental evidence
• Priming experiments (Does seeing/hearing one word
  facilitate recognition of another?) suggest
  neither
• Regularly inflected forms prime stem but not
  derived forms
• But spoken derived words can prime stems if they
  are semantically close (e.g. government/govern
  but not department/depart)

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• Speech errors suggest affixes must be represented
  separately in the mental lexicon
• easy enoughly

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What do we need to build a morphological parser?

• Lexicon list of stems and affixes (w/
  corresponding pos)
• 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)

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Using FSAs to Represent English Plural Nouns

• English nominal inflection

plural (-s)
reg-n
q0
q2
q1
irreg-pl-n
irreg-sg-n

• Inputs cats, geese, goose

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• Derivational morphology adjective fragment

adj-root1
-er, -ly, -est
un-
q5
adj-root1
q3
q4
?
-er, -est
adj-root2

• Adj-root1 clear, happy, real (clearly)
• Adj-root2 big, red (bigly)

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FSAs can also represent the Lexicon

• Expand each non-terminal arc in the previous FSA
  into a sub-lexicon FSA (e.g. adj_root2 big,
  red) and then expand each of these stems into
  its letters (e.g. red ? r e d) to get a
  recognizer for adjectives

e
r
q1
q2
un-
q3
q7
q0
b
d
q4
-er, -est
q5
g
q6
i
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But..

• Covering the whole lexicon this way will require
  very large FSAs with consequent search and
  maintenance problems
• Adding new items to the lexicon means recomputing
  the whole FSA
• Non-determinism
• FSAs tell us whether a word is in the language or
  not but usually we want to know more
• What is the stem?
• What are the affixes and what sort are they?
• We used this information to recognize the word
  can we get it back?

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Parsing with Finite State Transducers

• cats ?cat N PL (a plural NP)
• Koskenniemis two-level morphology
• Idea word is a relationship between lexical
  level (its morphemes) and surface level (its
  orthography)
• Morphological parsing find the mapping
  (transduction) between lexical and surface levels

c a t N PL
c a t s
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Finite State Transducers can represent this
mapping

• FSTs map between one set of symbols and another
  using an FSA whose alphabet ? is composed of
  pairs of symbols from input and output alphabets
• In general, FSTs can be used for
• Translators (HelloCiao)
• Parser/generator s(HelloHow may I help you?)
• As well as Kimmo-style morphological parsing

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• FST is a 5-tuple consisting of
• Q set of states q0,q1,q2,q3,q4
• ? an alphabet of complex symbols, each an i/o
  pair s.t. i ? I (an input alphabet) and o ? O (an
  output alphabet) and ? is in I x O
• q0 a start state
• F a set of final states in Q q4
• ?(q,io) a transition function mapping Q x ? to
  Q
• Emphatic Sheep ? Quizzical Cow

ao
bm
ao
ao
!?
q0
q4
q1
q2
q3
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FST for a 2-level Lexicon
cc
aa
tt

• E.g.

q3
q0
q1
q2
e
g
q4
q6
q7
q5
s
eo
eo
Reg-n Irreg-pl-n Irreg-sg-n
c a t g oe oe s e g o o s e
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FST for English Nominal Inflection
N?
reg-n
PLs
q1
q4
SG-
N?
irreg-n-sg
q0
q7
q2
q5
SG-
q3
q6
irreg-n-pl
PL-s
N?
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Useful Operations on Transducers

• Cascade running 2 FSTs in sequence
• Intersection represent the common transitions in
  FST1 and FST2 (ASR finding pronunciations)
• Composition apply FST2 transition function to
  result of FST1 transition function
• Inversion exchanging the input and output
  alphabets (recognize and generate with same FST)
• cf ATT FSM Toolkit and papers by Mohri, Pereira,
  and Riley

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Orthographic Rules and FSTs

• Define additional FSTs to implement rules such as
  consonant doubling (beg ? begging), e deletion
  (make ? making), e insertion (watch ? watches),
  etc.

Lexical f o x N PL
Intermediate f o x s
Surface f o x e s
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Porter Stemmer

• Used for tasks in which you only care about the
  stem
• IR, modeling given/new distinction, topic
  detection, document similarity
• Rewrite rules (e.g. misunderstanding --gt
  misunderstand --gt understand --gt )
• Not perfect . But sometimes it doesnt matter
  too much
• Fast and easy

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Summing Up

• FSTs provide a useful tool for implementing a
  standard model of morphological analysis, Kimmos
  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
• Read over HW1 and ask questions now