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Elaborazione del linguaggio naturale. Fabio Massimo Zanzotto. Part five. Feature Structures. Where we are?. Target of the analysis: interpret NL sentences with respect to a sort of anambiguos internal laguage - PowerPoint PPT Presentation
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FMZ
Elaborazione del linguaggio naturale
Fabio Massimo Zanzotto
FMZ
Part five
Feature Structures
FMZ
Where we are?
Target of the analysis:– interpret NL sentences with respect to a sort of
anambiguos internal laguage
• Natural Languages is a ambiguous and social beast vs. Formal Languages is unambiguous and “top-down” decided
• What’s a language model:– treating infinite sentences with generative machinaries
with a finite set of rules
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How we proceeded so far...
ASSIOM: A syntactic interpretation of a sentence helps in understanding its semantics
Let’s build a syntactic model for NL!!• Analysis of the chomsky hierarchy• Use of Context-free formalisms and related
parsing algoritms– CYK
– DCG (in prolog)
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How we proceeded so far...
OBSERVATION: NL is more difficult that what we may think
Let us renounce to the total grammaticality!!!
• Model offering Partial Analysis– CYK– Chart parsing and early algorithm
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Our Aim
Lines of development
Grammatical Representation Power: Build a formalism/model able to give the possibility of reducing the unnecessary interpretations
Grammar Use: Build a formalism (and an associated algorithm) able to represent partial analysis
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Our Aim
Lines of development
Grammatical Representation Power:
• CFG (context free grammars) DCG
Grammar Use:
• CYK
• Chart and Early Algorithm
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Observing natural language
Toy Examples:
... La vecchia porta la sbarra ...
... Il vecchio porta la sbarra ...
... Flying planes can be dangerous ...
... Flying planes is dangerous ...
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A sample Grammar (introspectively produced)
S NP VP | S SBAR | SBAR S
SBAR CongSub S
S S CongCoord S | S, S CongCoord S
NP NP SBAR
VP VerbX NP | VerbX NP PP
VerbX Verb | Modal Verb
NP Art Noun | Art Adj Noun | Noun | Verb Noun | NP PP
PP Prep NP
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Observations
• The sample grammar is insufficient!!• Spurious interpretations are produced for
unambiguous sentences• Loosing the eternal struggle between coverage and
induced ambiguity
NP Art Noun | Art Adj Noun | Noun | Verb Noun | NP PP
... the old man carries apples ...A) the old man (VP carries apples)B) the old man (NP carries apples)
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Necessary extensions
• Introducing notions like:– gender: masculine, feminine– number: singular, plural– person (for verbs)– time (for verbs)– mood (for verbs)
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Grammar
Adding number (Sing, Plur)
NPSing ArtSing NounSing
NPPlur ArtPlur NounPlur
VPSing VerbXSing NP | VerbXSing NP PP
VPPlur VerbXPlur NP | VerbXPlur NP PP
S NPSing VPSing | NPPlur VPPlur
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Grammar
Adding number (Sing, Plur) and gender (Mas, Fem)
NPMasSing ArtMasSing NounMasSing
NPFemSing ArtFemSing NounFemSing
NPMasPlur ArtMasPlur NounMasPlur
NPFemPlur ArtFemPlur NounFemPlur
VPSing VerbXSing NP | VerbXSing NP PP
VPPlur VerbXPlur NP | VerbXPlur NP PP
S NPMasSing VPSing | NPFemSing VPSing | NPMasPlur VPPlur | NPFemPlur VPPlur
!!Rules are uncontrollably proliferating!!
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Feature Structures
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What do we desire?
Adding number (Sing, Plur) and gender (Mas, Fem)
NPMasSing ArtMasSing NounMasSing
NPFemSing ArtFemSing NounFemSing
NPMasPlur ArtMasPlur NounMasPlur
NPFemPlur ArtFemPlur NounFemPlur
NP_Gen:X_Num:Y Art_Gen:X_Num:Y Noun_Gen:X_Num:Y
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Feature Structures
Feature structures (information containers) are:• Sets of attribute-value pairs• a value of an attribute may be:
– a final value (i.e., an element from a set)
– a feature structure
Agreement:
npCat:
Gen: mas
Num: sing
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Feature Structures
Formally if F is a feature structure,
• F is a set of pairs (f,v)
• given (f,v)F– v is a final value – v is a feature structure
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Feature Structures: Lexicon• nouns
– forma_superficiale– lemma– genere– numero
• verbs– forma_superficiale– radice– coniugazione: are, ere, ire– genere: mas, fem– numero: sing, plur– persona: 1,2,3– modo: indicativo, congiuntivo, imperativo– tempo: presente, passato,...– verso: attivo, passivo
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Lexicon: examples
forma_superficiale: mangeremo
radice: mangi
coniugazione: are
numero: plur
persona: 2
modo: indicativo
tempo: futuro
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Lexicon: examples
forma_superficiale: mangerebbe
radice: mangi
lemma: mangiare
coniugazione: are
numero: sing
persona: 3
modo: condizionale
tempo: presente
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Lexicon: examples
forma_superficiale: uomini
lemma: uomo
numero: plur
genere: mas
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How to use the lexicon?
“l’uomo mangierebbe pere”
that may be seen:[forma_supericiale: l’][forma_supericiale: uomo][forma_supericiale: mangierebbe] [forma_supericiale: pere]
forma_superficiale: mangierebbe
radice: mangi
lemma: mangiare
coniugazione: are
numero: sing
persona: 3
modo: condizionale
tempo: presente
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Comparing feature structures: subsumption
• A Feature Structure F1 subsumes F2 (F1 F2) if all the information that is in the F1is also in F2
Formally,
F1 F2 se e solo se
v = v’ oppure
(f,v) F1(f,v’) F2. v v’
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After the lexicon and the subsumption“l’uomo mangierebbe pere”
that may be seen:[forma_supericiale: l’][forma_supericiale: uomo][forma_supericiale: mangierebbe] [forma_supericiale: pere]
forma_superficiale: mangierebbe
radice: mangi
lemma: mangiare
coniugazione: are
numero: sing
persona: 3
modo: condizionale
tempo: presente
forma_superficiale: uomolemma: uomonumero: singgenere: mas
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What if?
“l’uomo mangierebbe pere”
that may be seen:[ forma_supericiale: l’][ forma_supericiale: uomo][ forma_supericiale: mangierebbe , forma_fonologica: xxxx ] [ forma_supericiale: pere]
forma_superficiale: mangierebbe
radice: mangi
lemma: mangiare
coniugazione: are
numero: sing
persona: 3
modo: condizionale
tempo: presenteSubsumption is not sufficient!
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Unification
Unification is a partial operation between two feature structures so that the new feature structure contain all the information of the two
F1F2 is so that:– F1 F1 F2
– F2 F1 F2
– if H has the property F1 H and F2 H then
F1 F2 H
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Unification Exampleforma_superficiale: mangierebbe
radice: mangi
lemma: mangiare
cat: verbo
coniugazione: are
numero: sing
persona: 3
modo: condizionale
tempo: presente
forma_superficiale: mangierebbeforma_fonologica: xxx
forma_superficiale: mangierebbe
radice: mangi
forma_fonologica: xxx
lemma: mangiare
cat: verbo
coniugazione: are
numero: sing
persona: 3
modo: condizionale
tempo: presente
=
=
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Unification
forma_superficiale: mangierebbe
radice: mangi
lemma: mangiare
cat: verbo
coniugazione: are
numero: sing
persona: 3
modo: condizionale
tempo: presente
forma_superficiale: mangiaforma_fonologica: xxx
Unification between two feature structures may not exist.
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Coindexing
What if we want to apply this rule?
forma_superficiale: mangierebbe
radice: mangi
lemma: mangiare
cat: verbo
coniugazione: are
numero: sing
persona: 3
modo: condizionale
tempo: presente
forma_superficiale: uomolemma: uomocat: nomenumero: singgenere: mas
cat: s cat: nomenumero: [1]
cat: verbonumero: [1]persona: 3
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Feature Structures in Prolog
• feature structures will be represented as a open list of attribute value pairs
• : (the colon) will be used to form attribute value pairs
es.[number:sg, person:3 | _ ]
[cat:np, agr:[number:sg, person:3 | _ ] | _ ]
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Unification in Prolog
unify0(Dag,Dag) :- !.
unify0([Feature:Value|Rest],Dag) :- val(Feature,Value,Dag,StripDag), unify0(Rest,StripDag). val(Feature,Value1,[Feature:Value2|Rest],Rest) :- !, unify0(Value1,Value2).val(Feature,Value,[Dag|Rest],[Dag|NewRest]) :- !, val(Feature,Value,Rest,NewRest).
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Where we worked today?
Lines of development
Grammatical Representation Power: Build a formalism/model able to give the possibility of reducing the unnecessary interpretations
Grammar Use: Build a formalism (and an associated algorithm) able to represent partial analysis