Computational Morphology and the META-NET Strategic Research Agenda for Multilingual Europe 2020
Morphology from a computational point of view
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Transcript of Morphology from a computational point of view
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Morphology from a computational point of view
March 2001
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Today
Minimal Edit Distance, and Viterbi more generally;
Letter to Sound What is morphology? Finite-state automata Finite-state phonological rules
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1. What is morphology?
Study of the internal structure of words: morph-ology word-s jump-ingWhy?1. For some purposes, we need to know
what the internal pieces are.2. Knowledge of the words of a language
can’t be summarized in a finite list: we need to know the principles of word-formation
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Some resources
Richard Sproat: Morphology and Computation (MIT Press, 1992)
Excellent overview of computational morphology and phonoloy by Harald Trost at
http://www.ai.univie.ac.at/~harald/handbook.html
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2. What applications need knowledge of words?Any high-level linguistic analysis:
syntactic parsermachine translation
speech recognition, text-to-speech (TTS)information retrieval (IR)dictionary, spell-checker
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3. A list is not enoughAn empirical fact:AP newswire: mid-Feb – Dec 30 1988Nearly 300,000 words.“New” words that appeared on Dec 31
1988:compounds: prenatal-care, publicly-
funded, channel-switching, owner-president, logic-loving, part-Vulcan, signal-emitting, landsite, government-aligned, armhole, signal-emitting...
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...new words...
dumbbells, groveled, fuzzier, oxidizedex-presidency, puppetry, boulderlike,
over-emphasized, hydrosulfite, outclassing, non-passengers, racialist, counterprograms, antiprejudice, re-unification, traumatological, refinancings, instrumenting, ex-critters, mega-lizard
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ex-presidency: prefix ex- boulder-like: suffix –like over-emphasized: prefix over- antiprejudice: prefix antiThis is often called the OOV problem
(“out of vocabulary”).
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If we work out the principles of word-formation, we will simultaneously:
1. compress the size of our internalized list of words;
2. become able to deal with new words on the fly.
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4. Overview of applications that need knowledge of words Speech generation: text to speech
(TTS) Speech recognition
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Text to speech
Problem: take text, in standard spelling, and produce a sequence of phonemes which can be synthesized by the “backend”.
Severe problems: Proper names (persons, places), OOV words
boathouse B OW1 T H AU2 S
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Speech recognition
Take a sound file (e.g., *.wav) and produce a list of words in standard orthography.
Bill Clinton is a recent ex-president. If someone says it, we need to figure out
what the word was.
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Do we know what a word is?
This is actually not an easy question! – especially if we turn to Asian languages, without a tradition of putting in “white space” between “words”, as we do in the West.
German writes more compounds without white space than English does.
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Basic principles of morphology
For some purposes, we need to think about phonemes, while for others it’s more convenient to talk about letters.
For our purposes, I’ll talk about letters whenever we don’t need to specifically focus on phonemes.
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Morpheme
It is convenient to be able to talk about the pieces into which words may be broken, and linguists call these pieces morphemes: the smallest parts of a language that can be regularly assigned a meaning.
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Morphemes
Uncontroversial morphemes:door, dog, jump, -ing, -s, to
More controversial morphemessing/sang: s-ng + i/acut/cut: cut + PAST
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Classic distinctions in morphology:Analytic (isolating) languages:
– no morphology of derivational or inflectional sort.
Synthetic (inflecting) languages:– Agglutinative: 1 function per morpheme– Fusional: > 1 function per morpheme
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Agglutinative:Finnish Nominal Declension
talo 'the-house' kaup-pa 'the-shop'talo-ni 'my house' kaup-pa-ni 'my shop'talo-ssa 'in the-house' kaup-a-ssa 'in the-shop'talo-ssa-ni 'in my house’ kaup-a-ssa-ni 'in my shop'talo-i-ssa 'in the-houses’ kaup-o-i-ssa 'in the-shops'talo-i-ssa-ni 'in my houses’ kaup-o-i-ssa-ni 'in my shops'
Courtesy of Bucknell Univ. web page
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Fusional: LatinLatin Declension of hortus 'garden'
Singular PluralNominative (Subject) hort-us hort-iGenitive (of) hort-i hort-rumDative (for/to) hort-o hort-isAccusative (Direct Obj) hort-um hort-usVocative (Call) hort-e hort-iAblative (from/with) hort-o hort-is
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Morphemes vs. morphs
Some analysts distinguish between “morphemes” and “morphs”.
Morphemes are motivated by an analysis, and include “plural” and “past”
Morphs are strings of letters or phones that “realize” or “manifest” a morpheme.
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Free and bound morphemes
Free morphemes can form (free-standing) words; bound morphemes are only found in combination with other morphemes.
Examples?
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Functions of morphology
Derivational morphology: creates one lexeme from another
compute > computer > computerize > computerization
Inflectional morphology: creates the form of a lexeme that’s right for a sentence:
the nominative singular form of a noun; or the past 3rd person singular form of a verb.
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Word: an identifiable string of letters (or phonemes) sing
Word-form: a word with a specific set of syntactic and morphological features. The sing in I sing is 1st person sg, and is a distinct word-from from the sing in you sing.
Lexeme: a complete set of inflectionally related word-forms, including sing, sings, and sang
Lemma: a complete set of morphologically related lexemes: sing, sings, song, sang.
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A lexeme’s stem
In many languages (unlike English), constellations of word-forms forming a lexeme demand the recognition of a basic stem which does not stand freely as a word:
Italian ragazzo, ragazzi (boy, girl)ragazzi, ragazze (boys, girls)
ragazz-
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CompoundsCompounds are composed of 2 (or more)
words or stems Compounds: hot dog, White House,
bookstore, cherry-covered
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Languages vary in the amount of morphology they have and useEnglish has a lot of derivational
morphology and relatively little inflectional morphology
English verb’s inflectional forms: bare stem, -s, -ed, -ing
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European languages
Not uncommon for a verb to have 30 to 50+ forms:
marking tense, person and number of the subject
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Derivation Derivational morphology usually consists of
adding a prefix or suffix to a base (= stem).The base has a lexical category (it is a noun,
verb, adjective), and the suffix typically assigns a different category to the whole word.
sad ness
Adj
Noun -ness: suffix that takes an adjective, & makes a noun.
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un interest ing
Adj
Verb
Adj
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Distinct from contractions…
English (and some other languages) permit the collapsing together of common words. In some extremely rare cases, only the collapsed form exists (English possessive ’s).
He will arrive tonight > he’ll arrive…The [King of England]’s children
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Some basics of English morphologyInflectional morphologyNouns: -NULL, -s, -’sVerbs: -NULL, s, -ed, -ing (so-called weak verbs)Strong verbs: 3 major groupsa. Internal verb change (sing/sang,
drive/drove/driven, dive/dove)b. –t suffix, typically with vowel-shortening
dream/dreamt, sleep/sleptc. –aught replacement: catch, teach,
seek,
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Derivational morphology in complexThis morphology creates new words, by
adding prefixes or suffixes.It is helpful to divide them into two groups,
depending on whether they leave the pronunciation of the base unchanged or not.
There are, as always, some fuzzy cases.
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Level 1
ize, ization, al, ity, al, ic, al, ity, ion, y (nominaliz-ing), al, ate, ous, ive, ation
Can attach to non-word stems (fratern-al, paternal; parent-al)
Typically change stress and vowel quality of stem
Level 2Never precede Level 1
suffixesNever change stress
pattern or vowel qualityAlmost always attach to
words that already existhood, ness, ly, s, ing, ish,
ful, ly, ize, less, y (adj.)
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Combinations of Class 1,2
Class 1 + Class 1: histor-ic-al, illumina-at-tion, indetermin-at-y;
Class 1 + Class 2: frantern-al-ly, transform-ate-ion-less;
Class 2 + Class 2: weight-less-ness ?? Class 2 + Class 1: *weight-less-ity,
fatal-ism-al
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Signature
Set of suffixes (or prefixes) that occurs in a corpus with a set of stems.
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NULL.ed.ing.s look interest add claim mark extend demand
remain want succeed record offer represent cover return end explain follow help belong attempt talk fear happen assault account point award appeal train contract result request staff view fail kick visit confront attack comment sponsor
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NULL.s paper retain improvement missile song truth doctor
indictment window conductor dick misunderstanding struggle stake tank belief cafeteria material mind operator bassi lot movement chain notion marriage dancer scholarship reservoir sweet right battalion hold mr shot cardinal athletic revenue duel confrontation solo talent guest shoe russian commitment average monk election street roger rifle worker area plane pinch-hitter dozen browning conclusion teacher narcotic appearance alternative dealer producer mile stock shrine sometime bag successor career mistake ankle weapon model front spotlight rhode pace debate payment requirement fairway consultation chip dollar employer thank mustang rocket-bomb hat string precinct robert employee action detective pressure measure spirit forbid hitter breast yankee partner floor member
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NULL.d.s increase tie hole associate reserve price fire receive
challenge rate purchase propose feature celebrate decide suite single change sculpture combine privilege pledge issue frame indicate believe damage include use aide graduate surprise intervene practice trouble serve oppose promise charge note schedule continue raise decline cause operate emphasize relieve hope share judge birdie produce exchange
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NULL.ed.er.ing.s report turn walk park pick flowNULL.d.ment enforce announce engage arrange replace
improve encourageNULL.n.s rose low take law drive rise undertakeNULL.al intern profession logic fat tradition extern margin
jurisdiction historic education promotion constitution addition sensation roy ration origin classic convention
NULL.man sand news police states gross sun fresh sports boss sales 3- patrol bonds
ed.er.ing slugg manag crush publish robbNULL.ity.s major senior moral hospitalNULL.ry hung mason ave summit scene surge rival forestNULL.a.s indian kind american
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Finite state morphology
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FSA: finite-state automataConsists of1. a set of states2. a starting state3. a set of final or accepting states4. a finite set of symbols5. a set of transitions: each is defined by
a from-state, a to-state, and a symbol
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It’s natural to think of this as describing an annotated directed graph.
q0 q1 q2 q3
b a a
q3
!a
a a !
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An FSA can be thought of as judging (accepting) a string, or as generating one.
How does it judge? Find a start to finish path that matches the string.
How does it generate? Walk through any start-to-finish path.
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Deterministic and Non-deterministic FSAsJust a little difference: Deterministic case: For every state,
there is a maximum of one transition associated with any given symbol. You can say that there’s a function from {states}X{symbols} {states}
Nondeterministic case: There is no such restriction; hence, given a state and a symbol, it is not necessarily certain which transition is to be taken.
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q0 q1 q2 q3
b a a
q3
!
a
deterministic…
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q0 q1 q2 q3
b a a
q3
!
a
non-deterministicThe best things in lifeare non-deterministic.
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Figure 3.4 p. 68
q0 q1 q2 q3
un- adj-root -er –est -ly
)()(
lyester
rootsadjun
Alternate notation
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Yet a third way: rows in an array(to-column can consist of pointers)
From To Output0 1 un0 1 NULL1 2 adj-root-list2 3 er;est;ly
Stop states: 2,3
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Figure 3.5 p. 69
q0q1 q2
q5
un-adj-root-1
-er –est -ly
q4 -er –est
q3adj-root-2
)()( 1
lyester
rootsadjun )(2
ester
rootsadj
adj-root-1
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Yet a third way: rows in an array(to-column can consist of pointers)
From To Output0 1 un0 3 NULL1 2 adj-root-list-1
2 5 er;est;ly3 2 adj-root-list-1
3 4 adj-root-list-2
4 5 er;est
Stop states: 2,4,5
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0 1 noun 11 2 ize2 3 ation2 4 er2 5 able0 5 -al “adj”0 1 adj-al5 6 ity;ness0 7 verbs17 8 ive “adj”8 6 ness8 9 ly0 10 verb210 8 ative0 11 noun211 8 ful
Figure 3.6, p. 70
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ation ize
noun
adj
verb
adverb
er
nouns
ativeive
able
ly ly
ity, ness
ful
verbs
adjectives
adverbs
We can simplify greatly (generating a bit more….)
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Finite-State Transducers (FST)The symbols of the FST are complex:
they’re really pairs of symbols, one for each of two “tapes” or levels.
Recognizer: decides if a given pair of representations fits together “OK”
Generator: generates pairs of representations that fit together
Translator: takes a representation on one level and produces the appropriate representation on the other level
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Finite state transducers
can be inverted, or composed and you get another FST.
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Complex symbols Usually of the form a:b, which means a
can appear on the upper tape when b appears on the lower tape.
So a:b means that’s a permissible pairing up of symbols.
“a” along means a:a, etc. epsilon means null character. Remember, “other” means “any feasible
pair that is not in this transducer” (p. 78)
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Using FSTs for orthographic rules
#__/ szsx
e
#
q0 q1 q2 q3 q4
q5:̂#
other
otherZ! = Z, s, x
Z! Z!
Z!
S
#, other
:e
#, other z,x
^:
^:
s
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Using FSTs for orthographic rules
fox^s#…we get to q1 with ‘x’#
q0 q1 q2 q3 q4
q5:̂#
other
otherZ! = Z, s, x
Z! Z!
Z!
S
#, other
:e
#, other z,x
^:
^:
s
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Using FSTs for orthographic rules
#
q0 q1 q2 q3 q4
q5:̂#
other
otherZ! = Z, s, x
Z! Z!
Z!
S
#, other
:e
#, other z,x
^:
^:
s
fox^s#…we get to q2 with ‘^’
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Using FSTs for orthographic rules
#
q0 q1 q2 q3 q4
q5:̂#
other
otherZ! = Z, s, x
Z! Z!
Z!
S
#, other
:e
#, other z,x
^:
^:
s
fox^s#…we can get to q3 with ‘NULL’
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Using FSTs for orthographic rules
#
q0 q1 q2 q3 q4
q5:̂#
other
otherZ! = Z, s, x
Z! Z!
Z!
S
#, other
:e
#, other z,x
^:
^:
s
fox^s#…we also get to q5 with ‘s’but we don’t want to!
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#
q0 q1 q2 q3 q4
q5:̂#
other
otherZ! = Z, s, x
Z! Z!
Z!
S
#, other
:e
#, other z,x
^:
^:
s
fox^s#…we also get to q5 with ‘s’but we don’t want to!
So why is this transition there??friend^ship, ?fox^s^s (= foxes’s)
![Page 62: Morphology from a computational point of view](https://reader035.fdocuments.us/reader035/viewer/2022081505/5681616c550346895dd0f909/html5/thumbnails/62.jpg)
#
q0 q1 q2 q3 q4
q5:̂#
other
otherZ! = Z, s, x
Z! Z!
Z!
S
#, other
:e
#, other z,x
^:
^:
s
fox^s#…q4 with s
![Page 63: Morphology from a computational point of view](https://reader035.fdocuments.us/reader035/viewer/2022081505/5681616c550346895dd0f909/html5/thumbnails/63.jpg)
#
q0 q1 q2 q3 q4
q5:̂#
other
otherZ! = Z, s, x
Z! Z!
Z!
S
#, other
:e
#, other z,x
^:
^:
s
fox^s#…q0 with # (accepting state)
![Page 64: Morphology from a computational point of view](https://reader035.fdocuments.us/reader035/viewer/2022081505/5681616c550346895dd0f909/html5/thumbnails/64.jpg)
#
q0 q1 q2 q3 q4
q5:̂#
other
otherZ! = Z, s, x
Z! Z!
Z!
S
#, other
:e
#, other z,x
^:
^:
s
arizona: we leave q0 but return
Other transitions…
![Page 65: Morphology from a computational point of view](https://reader035.fdocuments.us/reader035/viewer/2022081505/5681616c550346895dd0f909/html5/thumbnails/65.jpg)
#
q0 q1 q2 q3 q4
q5:̂#
other
otherZ! = Z, s, x
Z! Z!
Z!
S
#, other
:e
#, other z,x
^:
^:
s
m i s s ^ s
Other transitions…