8 november 2003 pp attachment problem1 prepositional phrase attachment problem 03m05601 ashish...

8 November 2003 PP attachment problem 1

Prepositional Phrase Attachment Problem

03M05601

Ashish Almeida


Overview

– Introduction to NLP– Analysis in UNL system– Prepositional phrase attachment problem– Proposed method to handle this problem


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


Tasks involved in NLP

Analysis and generation

Text Meaning

NL understanding

NL generation


Phases in NLP

• Morphological analysis• Syntactic analysis• Semantic analysis

• Discourse integration• Pragmatic analysis


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


Analysis

Morphological + Syntactic + Semantic analysis

• All these phases are dependent on each other.

• Interactive Vs modular approach

• Analysis in UNL system - interactive


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


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))


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.


Prepositional Phrase Attachment Problem

• Type of Structural ambiguity in a sentence

on new technologies.PP

the reportNP

readVP

JohnNP

Verb attachment

Noun attachment


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

*


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))


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


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.


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.


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>.


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”


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


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()


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)


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


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


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;


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

8 november 2003 pp attachment problem1 prepositional phrase attachment problem 03m05601 ashish...

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