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 Regularly Inflected Form• stem walk merge try

map• -s form walks merges tries maps• -ing participle walking merging trying

mapping• past; -ed participle walked merged tried

mapped

• Morph. Form Irregularly Inflected Form• stem eat catch cut • -s form eats catches cuts • -ing participle eating catching cutting • -ed past ate caught cut• -ed participle eaten caught cut

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• Noun Inflections for:• regular nouns (cat, hand); irregular nouns(child, ox)

• Morpholog. Form Regularly Inflected Form• stem cat hand• plural form cats hands

• Morph. Form Irregularly Inflected Form• stem child ox • plural form children oxen

Inflectional Morphology (nouns)

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Inflectional and Derivational Morphology (adjectives)

• Adjective Inflections and Derivations:

• prefix un- unhappy adjective, negation• suffix -ly happily adverb, manner• suffix -ier, -iest happier, happiest comparatives• suffix -ness 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 wordsBut “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 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

c:c a:a t:t +N: ε +PL: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

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We aren’t coveringthe overall schemein any more detail than this