cs626-449: nlp, speech and web-topics-in-ai pushpak bhattacharyya cse dept., iit bombay lecture 37:...

CS626-449: NLP, Speech and Web-Topics-in-AI

Pushpak BhattacharyyaCSE Dept., IIT Bombay

Lecture 37: Semantic Role Extraction (obtaining Dependency Parse)

Vaquious Triangle

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Anal

ysis

Generation

Transfer Based(do deep semantic processBefore entering the target language)

Direct(enter the target Language immediatelyThrough a dictionary)

Interlingua based (do deep semantic processBefore entering the target language)

Vaquious: an eminentFrench Machine Translation Researcher-Originally a Physicist

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Universal Networking Language

Universal Words (UWs) Relations Attributes Knowledge Base

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UNL Graph

obj

agt

@ entry @ past

minister(icl>person)

forward(icl>send)

mail(icl>collection)

He(icl>person)

@def

@def

gol

He forwarded the mail to the minister.

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AGT / AOJ / OBJ AGT  (Agent)

Definition:  Agt defines a thing which initiates an action

AOJ (Thing with attribute)Definition:  Aoj defines a thing which is in a state or has an attribute

OBJ (Affected thing)Definition: Obj defines a thing in focus which is directly affected by an event or state

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Examples John broke the window.

agt ( break.@entry.@past, John)

This flower is beautiful.aoj ( beautiful.@entry, flower)

He blamed John for the accident.obj ( blame.@entry.@past, John)

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BEN BEN (Beneficiary)

Definition:  Ben defines a not directly related beneficiary or victim of an event or state

Can I do anything for you?ben ( do.@entry.@interrogation.@politeness, you )obj ( do.@entry.@interrogation.@politeness,

anything )agt (do.@entry.@interrogation.@politeness, I )

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PUR PUR (Purpose or objective)

Definition:  Pur defines the purpose or objectives of the agent of an event or the purpose of a thing exist

This budget is for food.pur ( food.@entry, budget )mod ( budget, this )

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RSN

RSN (Reason)Definition:  Rsn defines a reason why an event or a state happens

They selected him for his honesty.agt(select(icl>choose).@entry, they)obj(select(icl>choose) .@entry, he)rsn (select(icl>choose).@entry, honesty)

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TIM

TIM (Time)Definition:  Tim defines the time an event occurs or a state is true

I wake up at noon.agt ( wake up.@entry, I )tim ( wake up.@entry, noon(icl>time))

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TMF TMF (Initial time)

Definition:  Tmf defines a time an event starts

The meeting started from morning.obj ( start.@entry.@past, meeting.@def )tmf ( start.@entry.@past, morning(icl>time) )

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TMT TMT (Final time)

Definition: Tmt defines a time an event ends

The meeting continued till evening.obj ( continue.@entry.@past, meeting.@def )tmt ( continue.@entry.@past,evening(icl>time) )

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PLC PLC (Place)

Definition:  Plc defines the place an event occurs or a state is true or a thing exists

He is very famous in India.aoj ( famous.@entry, he )man ( famous.@entry, very)plc ( famous.@entry, India)

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PLF

PLF  (Initial place)Definition:  Plf defines the place an event begins or a state becomes true

Participants come from the whole world.

agt ( come.@entry, participant.@pl )plf ( come.@entry, world )mod ( world, whole)

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PLT

PLT  (Final place)Definition:  Plt defines the place an event ends or a state becomes false

We will go to Delhi.agt ( go.@entry.@future, we )plt ( go.@entry.@future, Delhi)

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INS

INS   (Instrument) Definition:  Ins defines the instrument to carry out an event

I solved it with computeragt ( solve.@entry.@past, I )ins ( solve.@entry.@past, computer )obj ( solve.@entry.@past, it )

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Attributes Constitute syntax of UNL Play the role of bridging the conceptual world

and the real world in the UNL expressions Show how and when the speaker views what is

said and with what intention, feeling, and so on Seven types:

Time with respect to the speaker Aspects Speaker’s view of reference Speaker’s emphasis, focus, topic, etc. Convention Speaker’s attitudes Speaker’s feelings and viewpoints

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Tense: @past

The past tense is normally expressed by @past

{unl}agt(go.@entry.@past, he)…{/unl}

He went there yesterday

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Aspects: @progress

{unl}man

( rain.@entry.@present.@progress, hard )

{/unl}

It’s raining hard.

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Speaker’s view of reference

@def (Specific concept (already referred))The house on the corner is for sale.

@indef (Non-specific class)There is a book on the desk

@not is always attached to the UW which is negated.

He didn’t come. agt ( come.@entry.@past.@not, he )

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Speaker’s emphasis

@emphasisJohn his name is.

mod ( name, he )aoj ( John.@emphasis.@entry, name )

@entry denotes the entry point or main UW of an UNL expression

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Subcategorization Frames Specify the categorial class of the

lexical item. Specify the environment. Examples:

kick: [V; _ NP]cry: [V; _ ] rely: [V; _PP] put: [V; _ NP PP]think: : [V; _ S` ]

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Subcategorization Rules

V y /_NP]_ ]_PP]_NP PP]_S`]

Subcategorization Rule:

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Subcategorization Rules

1. S NP VP

2. VP V (NP) (PP) (S`)…3. NP Det N4. V rely / _PP]5. P on / _NP]6. Det the7. N boy, friend

The boy relied on the friend.

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Semantically Odd Constructions

Can we exclude these two ill-formed structures ? *The boy frightened sincerity. *Sincerity kicked the boy.

Selectional Restrictions

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Selectional Restrictions

Inherent Properties of Nouns:[+/- ABSTRACT], [+/- ANIMATE]

E.g., Sincerity [+ ABSTRACT]Boy [+ANIMATE]

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Selectional Rules A selectional rule specifies certain selectional

restrictions associated with a verb.

V y /[+/-ABSTARCT][+/-

ANIMATE]

V frighten

/ [+/-ABSTARCT]

[+ANIMATE]

__

__

__

__

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Subcategorization FrameforwardV__ NP PP

invitationN__ PP

accessibleA__ PP

e.g., An invitation to the party

e.g., A program making science is more accessible to young people

e.g., We will be forwarding our new catalogue to you

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Thematic Roles

The man forwarded the mail to the minister.

forward

V__ NP PP

Event FORWARD [Thing THE MAN], [Thing THE MAIL],

[Path TO THE MINISTER]

()

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How to define the UWs in UNL Knowledge-Base?

Nominal concept Abstract Concrete

Verbal concept Do Occur Be

Adjective concept Adverbial concept

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Nominal Concept: Abstract thing

abstract thing{(icl>thing)}culture(icl>abstract thing)

civilization(icl>culture{>abstract thing})direction(icl>abstract thing)

east(icl>direction{>abstract thing})duty(icl>abstract thing)

mission(icl>duty{>abstract thing})responsibility(icl>duty{>abstract thing})

accountability{(icl>responsibility>duty)}event(icl>abstract thing{,icl>time>abstract thing}) meeting(icl>event{>abstract thing,icl>group>abstract thing})

conference(icl>meeting{>event}) TV

conference{(icl>conference>meeting)}

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Nominal Concept: Concrete thing

concrete thing{(icl>thing,icl>place>thing)}building(icl>concrete thing)

factory(icl>building{>concrete thing})house(icl>building{>concrete thing})

substance(icl>concrete thing)cloth(icl>substance{>concrete thing})

cotton(icl>cloth{>substance})fiber(icl>substance{>concrete thing})

synthetic fiber{(icl>fiber>substance)} textile fiber{(icl>fiber>substance)}

liquid(icl>substance{>concrete thing})

beverage(icl>food,icl>liquid>substance}) coffee(icl>beverage{>food}) liquor(icl>beverage{>food})

beer(icl>liquor{>beverage})

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Verbal concept: do

do({icl>do,}agt>thing,gol>thing,obj>thing)express({icl>do(}agt>thing,gol>thing,obj>thing{)})

state(icl>express(agt>thing,gol>thing,obj>thing))

explain(icl>state(agt>thing,gol>thing,obj>thing))add({icl>do(}agt>thing,gol>thing,obj>thing{)})

change({icl>do(}agt>thing,gol>thing,obj>thing{)})

convert(icl>change(agt>thing,gol>thing,obj>thing)classify({icl>do(}agt>thing,gol>thing,obj>thing{)})

divide(icl>classify(agt>thing,gol>thing,obj>thing))

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Verbal concept: occur and be occur({icl>occur,}gol>thing,obj>thing)

melt({icl>occur(}gol>thing,obj>thing{)})

divide({icl>occur(}gol>thing,obj>thing{)})arrive({icl>occur(}obj>thing{)})

be({icl>be,}aoj>thing{,^obj>thing}) exist({icl>be(}aoj>thing{)})

born({icl>be(}aoj>thing{)})

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How to define the UWs in UNL

Knowledge Base?

In order to distinguish among the verb classes headed by 'do', 'occur' and 'be', the following features are used: 

UW[ need an agent ]

[ need an object ]

English

'do' + + "to kill"

'occur' - + "to fall"

'be' - - "to know"

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The verbal UWs (do, occur, be) also take some pre-defined semantic cases, as follows:

How to define the UWs in UNL Knowledge-Base?

UW PRE-DEFINED CASES

English

'do' takes necessarily agt>thing

"to kill"

'occur' takes necessarily obj>thing

"to fall"

'be' takes necessarily aoj>thing

"to know"

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Complex sentenceI want to watch this movie.

movie(icl>)

want (icl>)

@entry.@past

obj

@def

:01

I (iof>person)

watch (icl>do)@entry.@inf

objag

t

agt

I (iof>person)

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Approach to UNL Generation

Problem Definition Generate UNL expressions for English

sentences in a robust and scalable manner, using syntactic analysis and lexical

resources extensively. This needs

detecting semantically relatable entities and solving attachment problems

Semantically Relatable Sequences (SRS)

Definition: A semantically relatable Sequence (SRS) of a sentence is a group of words in the sentence (not necessarily consecutive) that appear in the semantic graph of the sentence as linked nodes or nodes with speech act labels

(This is motivated by UNL representation)

SRS as an intermediary to and intermediary

SourceLanguageSentence

TargetLanguageSentence

SRS UNL

Example to illustrate SRS

“The man bought a

new car in June” in: modifier

a: indefinite

the: definite

man

past tense

agent

bought

object

time

car

new

June

modifier

Sequences from “the man bought a new car in June”

a. {man, bought}b. {bought, car}c. {bought, in, June}d. {new, car}e. {the, man}f. {a, car}

Basic questions

Which words can form semantic constituents, which we call Semantically Relatable Sequences (SRS)?

What after all are the SRSs of the given sentence?

What semantic relations can link the words in an SRS and the SRSs themselves?

Postulate

A sentence needs to be broken into Sequences of at most three forms {CW, CW} {CW, FW, CW} {FW, CW}

where CW refers to content word or a clause and FW to function word

SRS and Language Phenomena

Movement: Preposition Stranding

John, we laughed at. (we , laughed.@entry)---------(CW,

CW) (laughed.@entry,at, John)---(CW, FW,

CW)

Movement: Topicalization

The problem, we solved. (we , solved.@entry)------------(CW,

CW) (solved.@entry , problem)-----

(CW,CW) (the, problem)--------------------(CW,CW)

Movement: Relative Clauses John told a joke which we had already heard.

(John, told.@entry) -------------------(CW, CW) (told.@entry, :01) ---------------------(CW,CW) SCOPE01(we,had,heard.@entry)-------(CW,

FW,CW) SCOPE01(already,heard.@entry)-------

(CW,CW) SCOPE01(heard@entry,which,joke)----

(CW,FW,CW) SCOPE01(a, joke)--------------------------(FW,CW)

Movement: Interrogatives Who did you refer her to?

(did , refer.@entry.@interrogative)-------(FW,CW)

(you, refer.@entry.@interrogative)--------(CW,CW)

(refer.@entry.@interrogative , her)--------(CW,CW)

(refer.@entry.@interrogative , to,who)----

(CW,FW,CW)

Empty Pronominals: to-infinitivals Bill was wise to sell the piano.

(wise.@entry , SCOPE01)---------------(CW,CW) SCOPE01(sell.@entry , piano)---------(CW,CW) (Bill, was, wise.@entry) -----------------(CW,

FW,CW) SCOPE01(Bill, to, sell.@entry)---------(CW,

FW,CW) SCOPE01(the, piano) --------------------(FW,CW)

Empty pronominal: Gerundial The cat leapt down spotting a thrush on the lawn. (The, cat) -------------------------------(FW, CW) (cat, leapt.@entry) --------------------(CW, CW) (leapt.@entry , down) ----------------(CW, CW) (leapt.@entry , SCOPE01) -----------------(CW, CW) SCOPE01(spotting.@entry,thrush)--------(CW,CW) SCOPE01(spotting.@entry,on,lawn)---(CW,FW,CW)

PP Attachment John cracked the glass with a stone.

(John, cracked.@entry)--------------(CW,CW) (cracked.@entry, glass)-------------(CW,CW) (cracked.@entry, with, stone)----

(CW,FW,CW) (a, stone)------------------------------(FW,CW) (the,glass)-------------------------(FW,CW)

SRS and PP attachment (Mohanty, Almeida, Bhattacharyya, 04)

Conditions Sub-conditions Attachment Point

[PP] is subcategorized by the verb [V]

[NP2] is licensed by a preposition [P]

[NP2] is attached to the verb [V] (e.g., He forwarded the mail to the minister)

[PP] is subcategorized by the noun in [NP1]

[NP2] is licensed by a preposition [P]

[NP2] is attached to the noun in [NP1](e.g., John published six articles on machine translation )

[PP] is neither subcategorized by the verb [V] nor by the noun in [NP1]

[NP2] refers to [PLACE] / [TIME] feature

[NP2] is attached to the verb [V](e.g., I saw Mary in her office; The girls met the teacher on different days)

Linguistic Study to Computation

Syntactic constituents to Semantic constituents

A probabilistic parser (Charniak, 04) is used.

Other resources: Wordnet and Oxford Advanced Learner’s Dictionary

In a parse tree, tags give indications of CW and FW: NP, VP, ADJP and ADVP CW PP (prepositional phrase), IN

(preposition) and DT (determiner) FW

Observation: Headwords of sibling nodes form SRSs

“John has bought

a car.”

SRS:{has, bought}, {a, car}, {bought, car}

a

(C) VP bought

(F) AUX has(C) VP bought

(C) VBD bought (C) NP car

(F) DT a (C) NN car

bought

car

has

Need: Resilience to wrong PP attachment

“John has published an

article on linguistics” Use PP attachment heuristics Get

{article, on, linguistics}

on linguistics

(C)VP published

(F) PP on(C)VBD published (C)NP article

published

(F)DT an

an

(C)NNarticle

(F)IN on

article

(C)NNS linguistics

(C)NPlinguistics

to-infinitival“I forced him to watch this movie” Clause boundary is the VP node, labeled with SCOPE

Tag is modified to TO, a FW tag, indicating that it heads a to-infinitival clause,

The duplication and insertion of the NP node with head him (depicted by shaded nodes)

as a sibling of the VBD node

with head forced is done to bring

out the existence of a semantic relation between force and

him.

(C)VP watch

(C)VBD forced (C)NP him(C) S SCOPE

(F)TO toto

(C)VP forced

to

forced

(C)VP

(C)PRP him

him

(C)NP him

him

(C)PRP him

Linking of clauses: “John said that he was reading a novel” Head of S node marked as Scope SRS: {said, that, SCOPE}.

Adverbial clauses have similar parse tree structures except that the subordinating conjunctions are different from that.

(C)VBD said (F) SBAR that

(C) VP said

(F) IN that(C) S SCOPE

said that

Implementation Block Diagram of the system

Parse Tree

Charniak Parser

Scope Handler

Attachment Resolver

WordNet 2.0

Sub-categorization Database

Input Sentence

Parse Tree modification and augmentation with head and scope

information

AugmentedParse Tree

Semantically Related Sequences

Noun classification

Semantically Relatable Sequences Generator

THAT clause as Subcat property

Preposition as Subcat property

Time and Place features

Head determination

Uses a bottom-up strategy to determine the headword for every node in the parse tree.

Crucial in obtaining the SRSs, since wrong head information may end up getting propagated all the way up the tree

Processes the children of every node starting from the rightmost child and checks the head information already specified against the node’s tag to determine the head of the node

Some special cases are: SBAR node A VP node with PRO insertion, copula, Phrasal verbs

etc. NP nodes with of-PP cases and conjunctions under

them, which lead to scope creation.

Scope handler

Performs modification on the parse trees by insertion of nodes in to-infinitival cases

Adjusts of the tag and head information in case of SBAR nodes

Attachment resolver

Takes a (CW1, FW, CW2) as input and checks the time and place features of CW2, the noun class of CW1 and the subcategorization information for the CW1 and

FW pair

to decide the attachment. If none of these yield any deterministic

results, take the attachment indicated by the parser

SRS generator

Performs a breadth-first search on the parse tree and performs detailed processing at every node N1 of the tree.

S nodes which dominate entire clauses (main or embedded) are treated as CWs.

SBAR and TO nodes are treated as FWs.

AlgorithmAlgorithmIf the node N1 is a CW (new/JJ,

published/VBD, fact/NN, boy/NN, John/NNP) perform the following checks:

If the sibling N2 of N 1 is a CW (car/NN, article/NN, SCOPE/S)

Then create {CW,CW} ({new, car}, {published, article}, {boy, SCOPE})

If the sibling N2 is a FW (in/PP, that/SBAR, and/CC)

Then, check if N2 has a child FW, N3 (in/IN, that/IN) and a child CW, N4 (June/NN, SCOPE/S)

If yes,Then use attachment resolver to decide

the CW to which N3 and N4 attach.Create{CW,FW,CW} ({published, in,

June}, {fact, that, SCOPE})If no,

Then check if next sibling N5 of N 1 is a CW (Mary/NN)

If yes,Create {CW,FW,CW} ({John, and, Mary})If the node N1 is a FW (the/DT, is/AUX,

to/TO), perform the following checks: If the parent node is a CW (boy/NP,

famous/VP)Check if sibling is an adjective.i. If yes, (famous/JJ)Then, create {CW,FW,CW} ({She, is,

famous})ii. If no, (boy/NN)Then, create {FW,CW} ({the, boy}, {has,

bought})If the parent node N6 is a FW (to/TO) and

the sibling node N7 is a CW (learn/VB)Use attachment resolver to decide on the

preceding CW to which N6 and N7 can attach.

Create {CW,FW,CW} ({exciting, to, learn})

Evaluation FrameNet corpus [Baker et. al., 1998], a

semantically annotated corpus, as the testdata.

92310 sentences (call this the gold standard)

Created automatically from the FrameNet corpus taking verbs, nouns and adjectives as the targets Verbs as the target- 37,984 (i.e., semantic

frames of verbs) Nouns as the target-37,240 Adjectives as the target-17,086

Score for high frequency verbsVerb Frequency ScoreSwim 280 0.709Depend 215 0.804Look 187 0.835Roll 173 0.7Rush 172 0.775Phone 162 0.695Reproduce 159 0.797Step 159 0.795Urge 157 0.765Avoid 152 0.789

Scores of 10 verb groups of high frequency in the Gold Standard

Scores of 10 noun groups of high frequency in the Gold Standard

An actual sentence

A. Sentence : A form of asbestos once used to make Kent cigarette filters has caused a high percentage of cancer deaths among a group of workers exposed to it more than 30 years ago, researchers reported.

Relative performance on SRS constructs

0 20 40 60 80 100

Total SRSs

(FW,CW)

(CW,FW,CW)

(CW,CW)

Par

amet

ers

mat

ched

Recall/Precision

Recall

Precision

Results on sentence constructs

0 20 40 60 80 100

To-infinitival clause resolution

Complement-clause resolution

Clause linkings

PP Resolution

Par

amet

er

Recall/Precision

Recall

Precision

Rajat Mohanty, Anupama Dutta and Pushpak Bhattacharyya, Semantically Relatable Sets: Building Blocks for Repesenting Semantics, 10th Machine Translation Summit ( MT Summit 05), Phuket, September, 2005.

Statistical Approach

Use SRL marked corpora Daniel Gildea and Daniel Jurafsky. 2002. Automatic labeling of

semantic roles. Computational Linguistics, 28(3):245–288.

PropBank corpus Role annotated WSJ part of Penn Treebank [10]

PropBank role-set [2,4] Core roles: ARG0 (Proto-agent), ARG1 (Proto-patient) to ARG5 Adjunctive roles:

ARGM-LOC (for locatives),

ARGM-TMP (for temporals), etc.

SRL marked corpora contd… PropBank roles: an example

[ARG0 It] operates] [ARG1 stores] [ARGM−LOC mostly in Iowa and Nebraska]

Preprocessing systems [2] Part of speech tagger Base Chunker Full syntactic parser Named entities recognizer

Fig.4: Parse tree output, Source: [5]

Probabilistic estimation [1] Empirical probability estimation over candidate roles for each

constituent based upon extracted features

here,

t is the target word

r is a candidate role,

h , pt, gov, voice are features

Linear interpolation, with condition

• Geometric mean, with condition

),,,,,(#

),,,,,,(#),,,,,|(

tvoicepositiongovpth

tvoicepositiongovpthrtvoicepositiongovpthrP

),,|()|(),,|(),|()|()|( 54321 tpthrPhrPtgovptrPtptrPtrPtconstituenrP

)},,|()|(),,|(),|()|(exp{1

)|( 54321 tpthrPhrPtgovptrPtptrPtrPz

tconstituenrP

1)|( r tconstituenrP

1i i

A state-of-art SRL system: ASSERT [4]

Main points [3,4] Use of Support Vector Machine [13] as classifier Similar to FrameNet “domains”, “Predicate Clusters” are introduced Named Entities [14] is used as a new feature

Experiment I (Parser dependency testing) Use of PropBank bracketed corpus Use of Charniak parser trained on Penn Treebank corpus

Parse Task Precision (%) Recall (%) F-score (%) Accuracy (%)

TreebankId. 97.5 96.1 96.8 -

Class. - - - 93.0

Id. + Class. 91.8 90.5 91.2 -

CharniakId. 87.8 84.1 85.9 -

Class. - - - 92.0

Id. + Class. 81.7 78.4 80.0 -

Table 1: Performance of ASSERT for Treebank and Charniak parser outputs.Id. Stands for identification task and Class. stands for classification task. Data source: [4]

Experiments and Results Experiment II (Cross genre testing)

1. Training on PropBanked WSJ data and testing on Brown Corpus

2. Charniak parser trained on first PropBank then Brown

Table 2: Performance of ASSERT for various experimental combinations Date source: [4]