Transcript (Chapter 3)(chapter3)

Morphological Analysis
Chapter 3
Morphology
• Morpheme = "minimal meaning-bearing unit in a language"
• Morphology handles the formation of words by using
morphemes
– base form (stem,lemma), e.g., believe
– affixes (suffixes, prefixes, infixes), e.g., un-, -able, -ly
• Morphological parsing = the task of recognizing the
morphemes inside a word
– e.g., hands, foxes, children
• Important for many tasks
– machine translation, information retrieval, etc.
– Parsing, text simplification, etc
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Morphemes and Words
• Combine morphemes to create words
 Inflection
 combination of a word stem with a grammatical morpheme
 same word class, e.g. clean (verb), clean-ing (verb)
 Derivation
 combination of a word stem with a grammatical morpheme
 Yields different word class, e.g delight (verb), delight-ful (adj)
 Compounding
 combination of multiple word stems
 Cliticization
 combination of a word stem with a clitic
 different words from different syntactic categories, e.g. I’ve = I +
have
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Inflectional Morphology
• Inflectional Morphology
• word stem + grammatical morpheme cat + s
• only for nouns, verbs, and some adjectives
• Nouns
 plural:

regular: +s, +es
irregular: mouse - mice; ox - oxen

many spelling rules: e.g. -y -> -ies like: butterfly - butterflies
 possessive: +'s, +'
• Verbs
 main verbs (sleep, eat, walk)
 modal verbs (can, will, should)
 primary verbs (be, have, do)
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Inflectional Morphology
(verbs)
•
•
Verb Inflections for:
main verbs (sleep, eat, walk); primary verbs (be, have, do)
•
•
•
•
•
Morpholog. Form
stem
-s form
-ing participle
past; -ed participle
Regularly Inflected
walk
walks
walking
walked
•
•
•
•
•
•
Morph. Form
stem
-s form
-ing participle
-ed past
-ed participle
Irregularly Inflected Form
eat
catch
eats
catches
eating
catching
ate
caught
eaten
caught
Form
merge
merges
merging
merged
try
tries
trying
tried
map
maps
mapping
mapped
cut
cuts
cutting
cut
cut
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Inflectional Morphology
(nouns)
• Noun Inflections for:
• regular nouns (cat, hand); irregular nouns(child, ox)
• Morpholog. Form
• stem
• plural form
Regularly Inflected Form
cat
hand
cats
hands
• Morph. Form
• stem
• plural form
Irregularly Inflected Form
child
ox
children
oxen
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Inflectional and Derivational
Morphology (adjectives)
• Adjective Inflections and Derivations:
•
•
•
•
prefix
suffix
suffix
suffix
un-ly
-ier, -iest
-ness
unhappy
adjective, negation
happily
adverb, manner
happier, happiest comparatives
happiness
noun
• plus combinations, like unhappiest, unhappiness.
• Distinguish different adjective classes, which can or cannot take
certain inflectional or derivational forms, e.g. no negation for
big.
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Derivational Morphology (nouns)
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Derivational Morphology
(adjectives)
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Verb Clitics
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Morpholgy and FSAs
• We’d like to use the machinery provided
by FSAs to capture these facts about
morphology
 Recognition:
 Accept strings that are in the language
 Reject strings that are not
 In a way that doesn’t require us to in effect
list all the words in the language
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Computational Lexicons
• Depending on the purpose, computational
lexicons have various types of information
 Between FrameNet and WordNet, we saw
POS, word sense, subcategorization, semantic
roles, and lexical semantic relations
 For our purposes now, we care about stems,
irregular forms, and information about affixes
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Starting Simply
• Let’s start simply:
 Regular singular nouns listed explicitly in
lexicon
 Regular plural nouns have an -s on the end
 Irregulars listed explicitly too
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Simple Rules
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Now Plug in the Words
Recognition of valid words
But “foxs” isn’t right; we’ll see how to fix that
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Parsing/Generation
vs. Recognition
• We can now run strings through these machines
to recognize strings in the language
• But recognition is usually not quite what we need
 Often if we find some string in the language we might
like to assign a structure to it (parsing)
 Or we might have some structure and we want to
produce a surface form for it (production/generation)
• Example
 From “cats” to “cat +N +PL”
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Finite State Transducers
• Add another tape
• Add extra symbols to the transitions
• On one tape we read “cats”, on the other
we write “cat +N +PL”
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FSTs
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Applications
• The kind of parsing we’re talking about is
normally called morphological analysis
• It can either be
• An important stand-alone component of many
applications (spelling correction, information
retrieval)
• Or simply a link in a chain of further linguistic
analysis
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Transitions
c:c
a:a
t:t
+N: ε
+PL:s
• 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
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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 different paths represent
different parses and different outputs will
result
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Ambiguity
• What’s the right parse (segmentation) for
• Unionizable
• Union-ize-able
• Un-ion-ize-able
• Each represents a valid path through the
morphology machine.
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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
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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
• Fox and Foxes vs. Cat and Cats
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Multi-Tape Machines
• To deal with these complications, we will
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
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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
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Intermediate to Surface
• The add an “e” rule as in fox^s# --> foxes#
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Lexical to Intermediate
Level
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Foxes
This arrow should
point straight down
#
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Notes
• The transducers may be run in the other
direction too (examples in lecture)
• The transducers are cascaded: The output of
one layer serves as the input to the next
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Overall Scheme
We aren’t covering
the overall scheme
in any more detail
than this
#
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