8 November 2003 PP attachment problem1 Prepositional Phrase Attachment Problem 03M05601 Ashish...
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8 November 2003 PP attachment problem 1
Prepositional Phrase Attachment Problem
03M05601
Ashish Almeida
8 November 2003 PP attachment problem 2
Overview
– Introduction to NLP– Analysis in UNL system– Prepositional phrase attachment problem– Proposed method to handle this problem
8 November 2003 PP attachment problem 3
Motivation
• Analysis involves many complex problems• Prepositional phrase attachment problem is
one such difficult problem.• If solved, improve the quality of information
extracted manifold• No existing system solves the problem
8 November 2003 PP attachment problem 4
Tasks involved in NLP
Analysis and generation
Text Meaning
NL understanding
NL generation
8 November 2003 PP attachment problem 5
Phases in NLP
• Morphological analysis• Syntactic analysis• Semantic analysis
• Discourse integration• Pragmatic analysis
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Is NL Compositional ?
• Compsitional expression– Meaning of the whole from meaning of
parts
e.g. strong tea
- rich tea
day by day
- all the time
8 November 2003 PP attachment problem 7
Analysis
Morphological + Syntactic + Semantic analysis
• All these phases are dependent on each other.
• Interactive Vs modular approach
• Analysis in UNL system - interactive
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UNL …• UNL is Interlingua
e.g. Ram ate rice with spoon.
agt
obj
spoon(icl>artifact)John(iof>person)
rice(icl>food)
eat(icl>do)@ entry@ present
ins
8 November 2003 PP attachment problem 9
UNL expresion
UNL Expression for Ram ate rice with spoon.agt(eat(icl>do).@past.@entry, Ram(iof>person))
obj(eat(icl>do).@past.@entry, rice(icl>food))
ins(eat(icl>do).@past.@entry, spoon(icl>tool))
Relation AttributesUWs
agt(eat(icl>do).@past.@entry, Ram(iof>person))
8 November 2003 PP attachment problem 10
Analysis in UNL
• Enconverter– Natural Language to UNL– Handles one sentence at a time– Predicate preserving parser– Kind of Turing machine
• Components– Dictionary : lexical units, uw, semantic attributes– Rule base : head movement rules, relation resolving rules
• Working – Uses dictionary and rule bases to process the sentence.
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Prepositional Phrase Attachment Problem
• Type of Structural ambiguity in a sentence
on new technologies.PP
the reportNP
readVP
JohnNP
Verb attachment
Noun attachment
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Prepositional Phrase Attachment Problem…
• Noun attachment Vs verb attachment
e.g. John read the report on new technologies. read
John the report
on
new technologies
read
John the report on
new technologies
*
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Establishing semantic relation
Same structure-different semantic relation
e.g.1. Ram ate rice with spoon. ……instrument
The UNL for this sentence is
ins(eat(icl>do).@past.@entry, spoon(icl>tool))
2. Ram ate rice with Sita. ……co-agentThe UNL for this sentence is
cag(eat(icl>do).@past.@entry, Sita(iof>person))
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Difficult problem
• PP attachment problem is simpler or no problem for human being
- who use world knowledge to process it.
• This world knowledge is not available to machines.
e.g. travel by night …time
travel by bus …instrument
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Different sites of attachment
– The search for the policy is going on.– The test will be held at the end of August.– In August 1947, India became free from British
rule.– Wilson received a medal from the commanding
officer at a farewell party.
• There is no restriction on how far the PP can lie from the word to which it relates.
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Affinity with preceding phrase
• The preposition of gets attached to a noun phrase or a verb phrase immediately preceding it.
– They were involved in the murder of a 90-year-old woman.– It was begun last week by the crew of a giant crane-barge.– He died of an overdose of sleeping pills– The system will be tailored to meet the need of the political
party.
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Existing methods
• generate mod-obj combination for almost all PP relations– E.g He came according to his promise.
agt(come(icl>do)@past.@entry, he)*mod(come(icl>do)@past.@entry, :01)obj:01(according to, promise(icl>abstract thing))mod:01(promise(icl>abstract thing),he)
• Tags introduced manually to resolve phrase boundaries – E.g. It delineates <p>the scope of phrases</p> before
<p>conversion of the sentence</p>.
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Related work
• Statistical learning methods used• Wordnet is used to find relations between words • Analysis of corpus is required• Not all aspects of problem considered
• The hypothesis does not apply to all cases
“PP attachments obey the principle of locality”
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Observations• Prepositions frequency is calculated from British
National Corpus• Classified into 2 parts
– Simple Preposition – Ambiguous prepositions
Frequency Preposition Poly. count
29391 of 718214 in 109343 to 814 by way of 116 by means of 1
8 November 2003 PP attachment problem 20
Addition to Semantic Attributes hierarchy
• Semantic attributes required to disambiguate • Addition required, if existing attributes fail to classify• necessary condition
– the attributes should be able to classify the semantically separate structures as separate entities.
e.g. the train for Delhi ….to()
the price for the Hill Road pool ….mod()
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Inclusion of preposition in UNL expression
• a picture on the wall plc(picture, wall).
• The cat walked across the street.– Wrong UNL
*plc ( walk, street )-cat walked along the street
-cat walked across the street – Correct UNLplc (walk, :01)obj:01(across, street)
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Classification based on syntax structure
• Sentences have different syntactic structure• Parsing the depends on surface structure
- Active-passive, transitive-di-transitive, present-past participles etc.
[ Verb + for + Noun phrase]v-pur He was waiting for the rainy day.v-pur He applied for a certificate. [ Noun phrase + for + Noun phrase] n-mod The search for the policy is going on.n-mod He pays the price for his indulgence.
• Classification based on syntax pattern
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Classification based on semantics• Deciding factors
– Syntax, attributes, preposition, subcategorisation frame(for verbs)
Partial list of preposition on and its possible semantic relation
Relation Example sentenceON
plc a picture on a wallins to travel on the bustim He came on Sundayseq Report to reception on arrivalmod a book on South Africains She played a tune on her guitarplc You can get me on 0181 530 3906
8 November 2003 PP attachment problem 24
Updating rule base• Simpler if the classification is perfect.• Issues involved
– Priority, proper specification
Two rules showing difference in priority – specific to general
Comment ;N/abs for N/abs ;search for policy
delete preposition for
DL(N,ABS){PRE,#FOR:::}{N,ABS:+PRERES,+FORRES,+pPUR::}P25;
Comment ;V FOR N-UNIT-QUARES ;suspend for 2 days
Delete preposition for
DL(VRB){PRE,#FOR:::}{N,UNIT,TIM,QUARES:+PRERES,+FORRES,+pDUR::}P30;
8 November 2003 PP attachment problem 25
Conclusion
• World knowledge is realized in terms of semantic attributes.
• Phrasal verbs are not considered• Idiomatic constructs are not handled
- e.g. day by day
all the time