Corpus-based Induction of an LFG Syntax-Semantics Interface for Frame Semantic Processing

Corpus-based Induction

of an LFG Syntax-Semantics Interface

for Frame Semantic Processing

Anette Frank, Jiří Semecký

[email protected]

[email protected]

LFG 2004, Christchurch 2July 10, 2004

Overview State of the art

Frame Semantics and FrameNet project Salsa frame annotation project LFG syntax-semantics interface for Frame Semantics

Our work Porting SALSA frame annotations to LFG Special phenomena Extraction of frame assignment rules

Conclusion Current data and results Summary Next steps [and Application]

OverviewState of the artOur workConclusion


Frame Semantics Frame Semantics (Fillmore 1976, 1977, ..)

Frame: a conceptual structure or prototypical situation,e.g. SPD requests that coalition talk about reform.

Evokes a frame REQUEST,with frame elements (frame semantic roles) that identify participants

SPEAKER, SPD ADDRESSEE, Coalition MESSAGE, talk about reform

Frame evoking elements: verbs, nouns, adjectives, ... introduce frames FrameNet

Berkeley FrameNet II Project Database of frames for a lexicon of English

Definition of frames and frame semantic roles Inheritance relations among frames Selected and manually annotated example sentence


Frame SemanticsSalsaFrom SALSA to LFG


SALSA Saarbrücken Lexical SemanticsAnnotation and Analysis Project

German FrameNet “light” Creating a large semantically annotated corpus of German Building on FrameNet DB definitions of frames and roles Strongly corpus-based oriented

Methods and Aims Manual annotation on top of syntactically annotated TIGER corpus (Semi-)automatic semantic annotation of larger corpora Automatic acquisition of a lexical semantic resource Semantics-based information access in NLP applications

Focus of our work Induction of an LFG syntax-semantics interface for frame semantics

from manually annotated corpus




SALSAExample

SPD fordert Koalition zu Gespräch über Reform auf.SPD requests that coalition talk about reform.

TIGER Newspaper corpus 1.5 Million words

TIGER annotation scheme Syntactic constituents Functional role labels (SB, HD, ..) Crossing edges (word order)




SALSAExample



SALSA frame annotation Frame evoking element, FEE,

(fordert auf) projects frame





SALSAExample




(fordert auf) projects frame Frame elements (FEs) of the

frame are connectedto syntactic constituents





SALSAExample




(fordert auf) projects frame Frame elements (FEs) of the

frame are connectedto syntactic constituents





From SALSA to LFG Automatic semantic frame assignment

Broad-coverage grammar High accuracy Portability of manual SALSA/TIGER frame annotations

German LFG grammar (IMS, Univ. Stuttgart) Used for TIGER annotation: 50% coverage, 70% precision Further extension of coverage OT-based and statistical disambiguation

A general syntax-semantics interface LFG f-structures provide a good level of abstraction PARGRAM: Common f-structure design principles for different

languages allow study of generalizations across languages





An LFG Frame Semantic ProjectionPorting SALSA Annotations to LFGSpecial phenomenaExtraction of Frame Assignment Rules

An LFG Frame Semantics Projection Projection from f-structure













An LFG Frame Semantics Projection

Description by Analysis:

transfer rule for frame projection

Co-description:

lexicon entry for frame projection

auffordern V,(PRED) = ‘AUFFORDERN <(SUBJ) (OBJ) (OBL OBJ)>’...( () FRAME) = REQUEST( () FEE) = ( PRED FN)( () SPEAKER) = ( SUBJ)( () ADDRESSEE) = ( OBJ)( () MESSAGE) = ( OBL OBJ)

pred (X, auffordern),subj (X, A), obj (X, B), obl (X, C), obj (C, D)==>+ (X, SemX), +frame (SemX, request),

+fee (SemX, auffordern),+ (A, SemA), +speaker (SemX, SemA),+ (B, SemB), +addressee (SemX, SemB),+ (D, SemD), +message (SemX, SemD),




Corpus-based inductionof frame assignment rules

Step 1: Porting SALSA annotations to LFG Using “parallel” LFG corpus of TIGER To obtain an LFG-frame corpus

Step 2: Induction of general frame assignment rules from the LFG-frame corpus

Can be applied to f-structure output of LFG parsing of new sentences




Porting SALSA Annotations to LFG

FRAME: Request

FEE ID: {2, 8}

SPEAKER:

ADDRESSEE:

MESSAGE:

1

3

501

Frame evoking elements (FEE) and frame elements (FE) connected to syntactic constituents identified by IDs

Extracting frame constituting information from SALSA/TIGER annotations FRAME, TIGER constituent IDentifiers of FEE and FEs



1 2 3 8

501


Porting SALSA Annotations to LFG

„Parallel“ TIGER corpus consisting of automatically derived LFG f-structures (Forst 2003)

Using treebank conversion methods Preserves TIGER constituent information (ID)



1 2 3 8

501


Porting SALSA Annotations to LFG An LFG Corpus with frame Semantic Projection

Identify f-structure nodes of FEE and FEs, using IDs as anchor Define semantic projection for frame and all the frame elements Using rewrite rules of XLE transfer system

OverviewState of the artOur workConsequences



Special Phenomena

Multiple constituents Asymmetric embedding

Coordination

Multiword expressions

Underspecification




Special PhenomenaMultiword Expressions

Idiomatic expression evokes frame for non-literal meaning „über die Ladentheke gehen“ -- „sell“

Project individual components to set-valued FEE-MWE

Vier Artikel gingen über die Ladentheke.Four items went over the counter“Four items were sold.”




Corpus-based induction of frame rules

Step 1: Porting SALSA annotations to LFG Using “parallel” LFG corpus of TIGER To obtain an LFG-frame corpus Rules anchored to node IDs

Step 2: Induction of general frame assignment rules from the LFG-frame corpus

Can be applied to f-structure output of LFG parsing of new sentences Rules anchored to functional descriptions

FE assignment (auffordern)(SUBJ) – SPEAKER(OBJ) – ADDRESSEE(OBL OBJ) – MESSAGE




Extraction of Functional Paths

FE assignment paths Paths relative to FEE

Local and non-local Non-local = with inside

out relative path Prefer local to non-local



SPD verspricht Wählern, Beschüsse mitzuteilen.SPD promises voters to report decisions. Relative f-path

FEE

MESSAGE OBJ local

SPEAKER(XCOMP ) SUBJ non-local

SUBJ local


Extraction of Functional Paths

Prefer local to non-local SPEAKER => choose SUBJ

In ambiguous non-local paths choose „shortest non-local sub-path“ Prefer (XCOMP ) SUBJ to (XCOMP XCOMP ) SUBJ

Non-local paths of equal length considered equally good Choose both (XCOMP ) OBJ and (ADJ ) OBJ



Relative f-path

FEE

MESSAGE OBJ local

SPEAKER(XCOMP ) SUBJ non-local

SUBJ local


Applying rules to new sentences mitteilen: COMMUNICATION; SUBJ SPEAKER, OBJ MESSAGE

Complete frames with all frame elements As instantiated in the corpus



Problem: unseen configurations (sparse data problem)

Partial annotation Individual rules for the FEE Individual rules for each FE of the frame (conditioned on FEE)

pred (X, mitteilen),‘SUBJ‘ (X, A),‘OBJ‘ (X, B)

+ (X, SemX), +frame (SemX, communication), +fee (SemX, mitteilen),+ (A, SemA), +speaker (SemX, SemA),+ (B, SemB), +message (SemX, SemB).

pred (X, mitteilen) + (X, SemX), +frame (SemX, communicaition),+fee (SemX, mitteilen).

pred (X, mitteilen), (X, SemX), frame (SemX, communicaition), ‘SUBJ‘(X, A)

+(A, SemA), +speaker (SemX, SemA).


Current Data and Results Data used:

12127 frame assignment rules 10009 sentences

Successfully ported frames: 11612 Compiled transfer rules after path extraction: 9334 Local vs. non-local FE assignments:

87.18% vs. 12.82%

Ambiguity rate: Average 8.83 rules per FEE Average 41.27 rules per frame


Current Data and ResultsSummaryNext steps and Application




Re-applying syntax-semantics mapping rules to TIGER-LFG corpus

Current Data and Results

Recall Precision Ambiguity rate / sentence

Full frame 93.98 % 25.94 % 8.46

Partial frame 94.98 % 45.52 % 7.83

Applying syntax-semantics mapping rules to free LFG parsing (without statistical disambiguation)

Recall Precision Ambiguity rate / sentence

Full frame 52.21 % 6.93 % 13.35

Partial frame 76.41 % 18.32 % 15.79


Summary

Modeling frame semantics in LFG framework Porting frame annotations from TIGER/SALSA to

an LFG corpus Extracting general frame assignment rules for LFG

parsing Applying frame assignment rules in an LFG parsing

architecture




Next steps

Semantically driven syntactic disambiguation Reduce ambiguity of syntactic parses Prefer parses with corresponding semantic annotation

Stochastic modeling for semantic role assignment Training stochastic models on the basis of corpus annotations For disambiguation of disjunctive frame assignments XLE: statistical ME package for training and online disambiguation


Current Data and ResultsSummary Next steps and Application

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Corpus-based Induction of an LFG Syntax-Semantics Interface for Frame Semantic Processing

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