Preposition Phrase Attachment in English Language Analysis

Preposition Phrase Preposition Phrase Attachment in Attachment in

English Language English Language AnalysisAnalysisAshish AlmeidaAshish Almeida

03M0560103M05601

April 19, 2023 2CFILT,IIT Bombay

PP attachmentPP attachment

John read the report on new technologies.John read the report on new technologies.

read

John the report

on

new technologies

PP attached to NPPP attached to NP

read

John the report

on

new technologies

PP attached to VPPP attached to VP

*


Same Structure: Same Structure: different rolesdifferent roles

Ram Ram ateate rice rice with a with a spoon.spoon. --instrumentinstrument

ins(ins(eateat(icl>do).@past.@entry, (icl>do).@past.@entry, spoonspoon(icl>tool))(icl>tool))

Ram Ram ateate rice rice with with Sita.Sita. -co-agent -co-agent

cag(cag(eateat(icl>do).@past.@entry, (icl>do).@past.@entry, SitaSita(iof>person))(iof>person))


UNL and EnConvertorUNL and EnConvertor

UNL is an intermediate language for UNL is an intermediate language for representing meaning of natural representing meaning of natural language.language.

EnConvertor is a language independent EnConvertor is a language independent parserparser

Rules are written for analysisRules are written for analysis UW Dictionary is created for analysisUW Dictionary is created for analysis PP-attachment is handled in EnCo.PP-attachment is handled in EnCo.


More about UNLMore about UNL

UNL graph ofUNL graph of John eats rice with a spoonJohn eats rice with a spoon

obj

agt

ins@ entry. @ present

rice(icl>food)

John(iof>person)

spoon(icl>artifact)

eat(icl>do)


AdjunctsAdjuncts

It provides extra information in a It provides extra information in a sentence.sentence.

It attaches to the verb.It attaches to the verb.

Examples:Examples: Ram came home.Ram came home.

Ram came homeRam came home on Monday. on Monday. Sita is sleeping.Sita is sleeping.

Sita is sleeping Sita is sleeping in the room.in the room.


Temporal Prepositional Temporal Prepositional PhrasesPhrases

Represents temporal information with the help of an objectRepresents temporal information with the help of an object

e.g. e.g. onon Sunday , Sunday , for for two days, two days, atat 5 p.m., 5 p.m., onon Diwali, Diwali, inin ice age, ice age, afterafter the meeting, the meeting, till till 6 O’ clock,6 O’ clock, overover two hours, two hours, beyondbeyond midnight midnight

Prepositions that can show temporality

at over through before for since by

in from during beyond till until to

on after between inside into within of


Time attributesTime attributes

To identify the type of time word To identify the type of time word

e.g. e.g. Attribute Examples

TIME Today, March

UNIT Day, second

EVENT Morning, Dinner

TIM_TOKEN am, pm

SECOND* 12, 15

DAY* Sunday; 23

POFDAY Dinner, Sunset

WEEK Week

ZDIM Start, End, Middle


UNL of Temporal UNL of Temporal PPsPPs

Different UNL generation in two different Different UNL generation in two different casescases

1.1. Delete the prepositionDelete the prepositione.g. e.g. come at nooncome at noon

tim(come , noon)tim(come , noon)

2.2. Retain the prepositionRetain the prepositione.g. e.g. come before nooncome before noon

tim(come, before)tim(come, before)

obj(before,noon)obj(before,noon)


Mapping from Prepositions Mapping from Prepositions to UNL relationsto UNL relations

Preposition UNL Relations Attributes of the Arguments

at tim [TIME,TIM_TOKEN]

in tim [N,TIME,MONTH][N,TIME,YEAR][N,POF_DAY]

on tim [N,TIME,DAY]

after tim-obj [TIME,TIM_TOKEN][N,TIME][N,EVENT]

before tim-obj [TIME,TIM_TOKEN][N,TIME][N,EVENT]


RulesRules

Rules for Rules for at-PPat-PP- Applies to “at 6 pm”- Applies to “at 6 pm”

;delete ;delete atatDL(VRB){PRE,#AT:::}

{TIME,TIM_TOKEN:+ATRES,+PRERES,+pTIM::}P22;

;create relation ;create relation timtim<{VRB:::}

{ATRES,PRERES,pTIM::tim:}P20;


TestingTesting

Wall Street Journal (WSJ) corpus is Wall Street Journal (WSJ) corpus is used.used.

Sentences from Oxford advanced Sentences from Oxford advanced learner’s Dictionary are also tested.learner’s Dictionary are also tested.

WSJ has V-N-P-N four-word WSJ has V-N-P-N four-word sentence fragmentssentence fragments

All temporal cases are tested for All temporal cases are tested for correctness of UNLcorrectness of UNL


ResultsResults#Total PPs 20801

#Temporal PPs 1326

#Cases of correct UNL

1112

Average accuracy 83.9 %

•Errors are mainly Errors are mainly due to due to mistakes in corpus mistakes in corpus and and inaccuracies in UW inaccuracies in UW dictionarydictionary


AssociativeAssociative ofof-PPPP

NP:NP: the comedy of Shakespeare the comedy of Shakespearemod(comedy(icl>abstract thing), mod(comedy(icl>abstract thing),

Shakespeare(iof >person))Shakespeare(iof >person))

NP:NP: the eyes of the boy the eyes of the boy pof(boy(icl>person), eye(pof>body).@pl)pof(boy(icl>person), eye(pof>body).@pl)

AP:AP: guilty of an offence guilty of an offence obj(guilty(aoj>thing), offence(icl>abstract thing)) obj(guilty(aoj>thing), offence(icl>abstract thing))

NP: NP: book of Rambook of Rampos(book(icl>concrete thing), pos(book(icl>concrete thing), Ram(iof>person))Ram(iof>person))


Partitive Partitive of-of-PPPP Here, in case of Here, in case of NN11-OF-N-OF-N22 the semantic head is the the semantic head is the

NN2.2.

The first NP indicates a quantityThe first NP indicates a quantity

ExamplesExamples

1.1. cup of teacup of tea qua(tea, cup)qua(tea, cup)

2.2. bag of orangesbag of oranges

3.3. bundle of sticksbundle of sticks

4.4. a pinch of salta pinch of salt Kind constructionKind construction

e.g. e.g. thatthat kind of people kind of people mod(people, kind)mod(people, kind) Kind-type words : Kind-type words : kind, type, sort, variety etc.etc.


Argument structure (AS)Argument structure (AS) Argument structure specify the structural Argument structure specify the structural

frame into which a verb can be fitted. frame into which a verb can be fitted. For example, For example,

*Ram *Ram sawsaw. . This is unacceptable as verb This is unacceptable as verb seesee has strict has strict subcategorisation feature (+ _NP). That is verb subcategorisation feature (+ _NP). That is verb

seesee takes NP as object. Thus a valid sentence is takes NP as object. Thus a valid sentence is

Ram Ram sawsaw Sita. Sita.

AS of see is (NP _ NP)AS of see is (NP _ NP)


Adjunct and Adjunct and ComplementsComplements

1.1. He gave a book He gave a book to Ramto Ram..give (NP _ NP give (NP _ NP toto-PP)-PP)

- without- without to Ram to Ram sentence is unacceptable sentence is unacceptable

- to Ram- to Ram is complement is complement

2.2. He gave a book He gave a book to Ramto Ram on Sundayon Sunday- - without without on Sunday on Sunday sentence is acceptable sentence is acceptable

- on Sunday- on Sunday is adjunct is adjunct

Similarly, nouns and adjectives take Similarly, nouns and adjectives take complementscomplements


More on ASMore on AS

Ram accused Sita Ram accused Sita of cheatingof cheating AS is (NP _ NP AS is (NP _ NP of-PPof-PP)) UNL for the sentence frame with the UNL for the sentence frame with the

verb accuse (agtverb accuse (agtNPNP _ obj _ objNPNP rsnrsnof-PPof-PP) )

Whereas for adjuncts, the case relations Whereas for adjuncts, the case relations differ.differ.

1.1. He saw the girl He saw the girl through the windowthrough the window..

2.2. He saw the girl He saw the girl with angerwith anger..

3.3. He saw the girl He saw the girl in the libraryin the library. .


Dictionary entryDictionary entry

He gave a book He gave a book toto Ram. Ram. The lexicon will have entry of The lexicon will have entry of gavegave which which

provides AS information.provides AS information. [gave] {} “give(icl>do)” (VRB,VOA,VOA-PHSL,[gave] {} “give(icl>do)” (VRB,VOA,VOA-PHSL,

#_TO#_TO, , #_TO_GOL#_TO_GOL,PAST) <E,0,0>;,PAST) <E,0,0>;

PP in the sentence can be identified as a PP in the sentence can be identified as a complement complement or not.or not.

This solves the attachment in some cases.This solves the attachment in some cases.


Verb attachment of Verb attachment of ‘of-‘of-PP’PP’

Three possible casesThree possible cases

V

NP1 NP2

V attaches to NP1

V attaches to NP2

(A)

V

NP1 NP2

V attaches to NP1

NP1 attaches to NP2

(B)

V

NP1 NP2

V attaches to NP2

NP2 attaches to NP1

(C)


of-PPof-PP attachment cases attachment cases

... remind him of Gita

... saw the book of physics

... drank a cup of milk

Case A

Case B

Case C


Four cases of attachmentFour cases of attachment Attachment in presence or absence of attribute Attachment in presence or absence of attribute ofof

Attributes of V

Attributes of NP1

Attachment of NP2

1 V,OF N,OF NP1

2 V,ÔF N,OF NP1

3 V,OF N,ÔF V

4 V,ÔF N,ÔF NP1

•


RulesRules

;Noun attachment;Noun attachment

R{VRB,#_OF:::}{N,#_OF:::}(PRE,#OF)P60;

;Noun attachment;Noun attachment

R{VRB,^#_OF:::}{N:::}(PRE,#OF)P60;

;Verb attachment, first resolve the ;Verb attachment, first resolve the immediate objectimmediate object

<{VRB,#_OF,#_OF_OBJ:::}{N,^#_OF::obj:}

(PRE,#OF)P30;


Testing processTesting process

British National Corpus (BNC) is used.British National Corpus (BNC) is used. V-N-V-N-ofof-N type sentences are extracted -N type sentences are extracted

from corpus.from corpus. AS information is added to verbs and AS information is added to verbs and

nouns using Oxford Advanced nouns using Oxford Advanced Learner’s Dictionary and Beth Levin’s Learner’s Dictionary and Beth Levin’s verb classes.verb classes.

AS information is merged into the AS information is merged into the Dictionary.Dictionary.


ResultsResults

Total Correct Incorrect

V-attachment 7 7 0

N-attachment (all) 493 439 54

Associative cases 411 362 49

Partitive cases 82 (16%) 77 5

Total 500 446 54


To sum upTo sum up

The PP attachment problem is The PP attachment problem is successfully divided in two parts – successfully divided in two parts – complement PPs and adjunct PPscomplement PPs and adjunct PPs

This division clearly delineates the This division clearly delineates the problem so that their analysis does not problem so that their analysis does not conflict.conflict.

Analysis of complements will be mostly Analysis of complements will be mostly driven by rich attribute set from lexicondriven by rich attribute set from lexicon

Whereas analysis of adjuncts will be Whereas analysis of adjuncts will be driven by rulesdriven by rules


ContributionContribution

Deciding the overall strategy of analysisDeciding the overall strategy of analysis Providing computation insights in Providing computation insights in

linguistic analysislinguistic analysis Design of attribute set/introducing new Design of attribute set/introducing new

attributesattributes Design and implementation of rules for Design and implementation of rules for

EnCo.EnCo. Testing of sentences.Testing of sentences. Dictionary corrections/modificationsDictionary corrections/modifications


Future workFuture work

Focus will be on complements.Focus will be on complements. Also, that-clause, to and -ing infinite Also, that-clause, to and -ing infinite

clause handling, PRO detection will clause handling, PRO detection will be tried in similar fashion.be tried in similar fashion.

Automatic/semi-automatic Automatic/semi-automatic acquisition of AS information for acquisition of AS information for dictionaries will be tried out. dictionaries will be tried out.

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Preposition Phrase Attachment in English Language Analysis

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