Transcript 英文を対象とする未知語の 意味属性推定機能と 漸進的

自然言語処理2010
東京工科大学
コンピュータサイエンス学部
亀田弘之
今日の話題
• 研究レベルの話題に挑戦！
• 機械学習（帰納論理プログラミングの紹介）
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研究レベルの話題に挑戦！
• Pacling2009 での研究発表を理解してみよう！
– 皆さんはもうこのレベルの話しを理解できます。
– 批判的に聞いてください。
– 自分なりのアイデアを得てください。
（アイデアを得たらそれをもとに
NLPの研究をしよう！）
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Unknown Word Acquisition
by Reasoning Word Meanings
Incrementally and Evolutionarily
Hiroyuki Kameda
Tokyo University of Technology
Chiaki Kubomura
Yamano College of Aesthetics
Overview
1.
2.
3.
4.
Research background
Basic ideas of knowledge acquisition
Demonstrations
Concludings
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Exchange of
Information &
Knowledge
Language etc.
natural & smooth
communication
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A talking Robot with a human
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unknown words
new social events
natural, smooth
Dialogue
and
flexible
（communication
Various Topics）
yougers’ words
New products release
Professional Slangs
Highly upgraded
NLP technology
Unknown word
Processing
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Unknown Word Acquisition
-Our Basic ideas-
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Information Processing Model
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NLP System
Text: Dogs ran fast.
Syntactic structure:
S-----NP-----N-----dogs
|
----VP----V----ran
|
--Adv---fast
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Information Processing Model 2
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Knowledge Acquisition Model
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Knowledge Acquisiton System
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More concretely to say…
Program(Source codes)
（Knowledge of objects
to be processed）
Processing rule
acquisition
trigger
sentence
NLP Engine
Internal
representation
trigger
Lexicon
（Domain Knowledge）
UW Acquisition
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Further more concretely to say…
Rule-based grammar＋α
（Target Knowledge）
Syntactic rule
acquisition
（by ILP）
Batch
sentence
NLP Engine
（Prolog Interpreter）
Internal
representation
Failuretrigger
Lexicon
（Domain Knowledge）
UW Acquisition
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Further more concretely to say…2
Rule-based grammar＋α
（Target Knowledge）
Syntactic rule
acquisition
（by ILP）
Batch
sentence
NLP Engine
（Prolog Interpreter）
Internal
representation
Failuretrigger
Lexicon
（Domain Knowledge）
UW Acquisition
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Main topics
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Let’s consider a grammar.
G1 = { Vn, Vt, s, P },
where
Vn = {noun, verb, adverb, sentence},
Vt = {dogs, run, fast},
s = sentence,
P = { sentence → noun + verb + adverb,
noun→dogs, verb→run, adverb →fast}.
○ dogs run fast.
× cars run fast.
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Let’s consider a grammar.
Grammar G1 = { Vn, Vt, s, P },
where
Vn = {noun, verb, adverb, sentence},
Vt = {dogs, run, fast},
s = sentence,
P = { sentence → noun + verb + adverb,
noun→dogs, verb→run, adverb →fast}.
○ dogs run fast.
× cars run fast.
Cannot unify!
cars <=!=> noun
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Our ideas
1. Processing modes
2. Processing strategies
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Processing Modes
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Processing Modes
Modes
Unknow Unknow
Syntactic
Words rules
Mode１
No
No
Mode２
Yes
No
Mode3
No
Yes
Mode4
Yes
Yes
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Processing Modes
Modes
Unknow Unknow
Syntactic
Words rules
Mode１
No
No
Mode２
Yes
No
No
Yes
Yes
Yes
×
Mode4
×
Mode3
◎
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Processing Modes
Modes
Unknow Unknow
Syntactic
Words rules
Mode１
No
No
Mode２
Yes
No
×
Mode4
×
Mode3
No
Yes
◎
Yes
○ dogs run fast.
×Yes
cars run fast.
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Processing Strategies
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Adopted Processing Strategies
1. Parse a sentence in mode-1 at first.
2. If parsing fails, then switch the processing
mode from mode-1 to mode-2.
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Grammar G1 in Prolog
Syntactic rule
sentence (A,D):noun (A,B), verb (B,C), adverb (C,D).
noun([dogs|T],T).
verb([run|T],T).
adverb([fast|T],T).
G1 = { Vn, Vt, s, P },
where
Vn = {noun, verb,Lexicon
adverb, sentence},
Vt = {dogs run fast.},
s = sentence,
New processing rule
P = { sentence → noun + verb + adverb,
noun→dogs, verb→run, adverb →fast}.
noun(AT,T) :- write(‘Unknown word found!‘).
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References
• Kameda, Sakurai and Kubomura：
ACAI’99 Machine Learning and
Applications, Proceedings of Workshop
W01: Machine learning in human language
technology, pp.62-67(1999).
• Kameda & Kubomura:
Proc. of Pacling2001, pp.146-152(2001).
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Let’s explain in more details!
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Example sentence
Tom broke the cup with the hammer.
(Okada1991)
tom broke the cup with the hammer
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Grammatical settings
G2 = <Vn, Vt, P, s>
Vn = { s, np, vp, prpn, v, prp, pr, det, n },
Vt = { broke, cup, hammer, the, tom, with }
P = { s -> np,vp. np -> prpn. vp -> v,np,prp.
prp -> pr,np.
np -> det,n.
prpn -> tom.
V -> broke.
Det -> the. n -> cup. pr -> with.
n -> hammer. }
s:start symbol
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Prolog version of Grammar G2
/* Syntactic rules*/
s(A, C, s( _np, _vp ), a1( _act, _agt, _obj, _inst )) :np( A, B, _np, sem( _agt ) ),
vp( B, C, _vp, sem( _act, _agt, _obj, _inst )).
np(A, B, np( _prpn ), sem( _ )) :prpn(A, B, _prpn, sem( _ )).
vp(A, D, vp( _v, _np, _prp ), sem( Act, Agt, Obj,
Inst )) :v( A, B, _v, sem( Act, Agt, Obj, Inst )),
np( B, C, _np, sem( Obj )),
prp( C, D, _prp, sem( Inst )).
vp(A, C, vp( _v, _np ), sem(Act,Agt,Obj,Inst) ) :v(A, B, _v, sem( Act, Agt, Obj, Inst ) ),
np(B, C, _np, sem( Obj )).
prp(A, C, prp( _pr, _np ), sem( Z )) :pr(A, B, _pr, sem( _ ) ),
np(B, C, _np, sem( Z )).
np(A, C, np( _det, _n ), sem( W )) :det(A, B, _det, sem( _ ) ),
n(B, C, _n, sem( W )).
/* Lexicon*/
prpn( [tom|T], T, prpn(tom), sem(human) ).
v([broke|T],T, v1(broke),
sem(change, in_shape, human, thing, tool) ).
det([the|T], T, det(the), sem( _ ) ).
n( [cup|T], T, n(cup), sem(thing) ).
pr( [with|T], T, pr(with), sem( _ )).
n( [hammer|T], T, n(hammer), sem(tool) ).
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Demonstration（Mode１）
• Input 1：
[tom,broke,the,cup,with,the,hammer]
• Input 2：
[tom,broke,the,glass,with,the,hammer]
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Problem
• Parsing fails, when unknown words exist
in sentences.
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Unknown word Processing
• Switching processing modes
（from Mode-1 to Mode-2）
• When fails, switch the processing mode
from mode-1 to mode-2.
• Execute the predicate assert of Prolog to
change the mode.
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Demonstration（Mode2）
• Input：
– P1: [tom,broke,the,cup,with,the,hammer]
– P2: [tom,broke,the,glass,with,the,hammer]
– P3: [tom,broke,the,glass,with,the,stone]
– P4: [tom,vvv,the,glass,with,the,hammer]
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Problem
• Leaning is sometimes imperfect.
• Learnig order influences learnig results.
• Solution：
Influence of learning order is covered with
introducing a function of evolutionary
learning
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More Explanations
• All information of unknown words should
be guessed, when the unknown words are
registered to lexicon.
spelling and POS are guessed, but not
pronunciation. (imperfect knowledge)
• If the pronunciation can be guessed later,
the information will be added to lexicon.
→ Evolutionary Learning!
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Solution
• Setting(some knowledge may be revised
but some must not)
– a priori knowledge (Initial Knowledge)：
must not change
– posterior knowledge(Acquired Knowledge):
• Must not change, if perfect
• May change, if imperfect
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Demonstration of Final version
• Input：
– P4: [tom,vvv,the,glass,with,the,hammer]
– P2: [tom,broke,the,glass,with,the,hammer]
– P3: [tom,broke,the,glass,with,the,stone]
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Concludings
1. Research background
2. Basic ideas of knowledge acquisition
1. Some models
1. Information processing model
2. Unknown word acquisition model
2. Modes and Strategies
3. Demonstrations
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Future Works
• Applying to more real world domain
– Therapeutic robots
– Robot for schizophrenia rehabilitation
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次の話題は？
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機械学習
• ILPの紹介
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