October 2004 CSA3050 NLP Algorithms 1 CSA3050: Natural Language Algorithms Morphological Parsing
Mar 19, 2016
October 2004 CSA3050 NLP Algorithms 1
CSA3050: Natural Language Algorithms
Morphological Parsing
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Morphology
• Morphemes: The smallest unit in a word that bear some meaning, such as rabbit and s, are called morphemes.
• Combination of morphemes to form words that are legal in some language.
• Two kinds of morphology– Inflectional– Derivational
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Inflectional/DerivationalMorphology
• Inflectional+s plural+ed past
• category preserving• productive: always
applies (esp. new words, e.g. fax)
• systematic: same semantic effect
• Derivational+ment
• category changingescape+ment
• not completely productive: detractment*
• not completely systematic: apartment
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Noun Inflections
Regular Irregular
Singular cat church mouse ox
Plural cats churches mice oxen
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Morphological Parsing
MorphologicalParser
Input Word
cats
OutputAnalysis
cat N PL
• Output is a string of morphemes• Reversibility?
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Morphological Parsing
• The goal of morphological parsing is to find out what morphemes a given word is built from. mouse mouse N SGmice mouse N PLfoxes fox N PL
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2 Steps1. Split word up into its possible components,
using + to indicate possible morpheme boundaries.
cats cat + sfoxes fox + sfoxes foxe + s
2. Look up the categories of the stems and the meaning of the affixes, using a lexicon of stems and affixes
cat + s cat + NP + PLfox + s fox + N + PL.
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Step 1: Surface IntermediateFST
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Step 1: Surface IntermediateOperation
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2. Intermediate Morphemes
Possible inputs to the transducer are:
• Regular noun stem: cat• Regular noun stem + s: cat+s• Singular irregular noun stem: mouse• Plural irregular noun stem: mice
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2. Intermediate MorphemesTransducer
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Handling Stems
cat /cat
mice/mouse
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Completed Stage 2
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Joining Stages 1 and 2
• If the two transducers run in a cascade (i.e. we let the second transducer run on the output of the first one), we can do a morphological parse of (some) English noun phrases.
• We can change also the direction of translation (in translation mode).
• This transducer can also be used for generating a surface form from an underlying form.
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Prolog• The transducer
specifications we have seen translate easily into Prolog format except for the other transition.
• arc(1,3,z:z).arc(1,3,s:s).arc(1,3,x:x).arc(1,2,#:+).arc(1,3,<other>).
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Handling other arcs
arc(1,3,z:z) :- !.arc(1,3,s:s) :- !.arc(1,3,x:x) :- !.arc(1,2,#:+) :- !.arc(1,3,X:X) :- !.
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Combining Rules• Consider the word “berries”.• Two rules are involved
– berry + s– y → ie under certain circumstances.
• Combinations of such rules can be handled in two ways– Cascade, i.e. sequentially– Parallel
• Algorithms exist for combining transducers together in series or in parallel.
• Such algorithms involve computations over regular relations.
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3 Related Frameworks
REGULARLANGUAGES
REGULAREXPRESSIONS
FSA
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REGULAR RELATIONS
REGULARRELATIONS
AUGMENTEDREGULAR
EXPRESSIONS
FINITE STATETRANSDUCERS
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Putting it all together
execution of FSTi
takes place in parallel
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Kaplan and KayThe Xerox View
FSTi are alignedbut separate
FSTi intersectedtogether
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Summary
• Morphological processing can be handled by finite state machinery
• Finite State Transducers are formally very similar to Finite State Automata.
• They are formally equivalent to regular relations, i.e. sets of pairings of sentences of regular languages.