on distance between deep syntax and semantic representation

MotivationIssues of transformation

Conclusions

On Distance between Deep Syntax and SemanticRepresentation

Vaclav Novak

Institute of Formal and Applied LinguisticsCharles University

Prague, Czech Republic

Frontiers in Linguistically Annotated CorporaJuly 22, 2006, 16:00 – 16:30

Sydney, Australia

[email protected] Syntax – Semantic Distance 1/ 20


Conclusions



Conclusions

Presentation Outline

Again

1 MotivationMultiNet – Knowledge RepresentationPrague Dependency TreebankMissing pieces

2 Issues of transformationMappingTopic-Focus ArticulationAdditional Requirements

3 ConclusionsConclusionsRelated WorkFuture Work



Conclusions

MultiNet – Knowledge RepresentationPrague Dependency TreebankMissing pieces

MultiNet

What is MultiNet

Multilayered Semantic Network

University in Hagen, Germany

Hermann Helbig, Sven Hartrumpf

Parser: WOCADI for German(relies heavily on HaGenLex lexicon)

MWR interface (Workbench of Knowledge Engineer)

Designed w.r.t. question answering and cognitive modeling



Conclusions


Semantic Network

Properties of Semantic Networks

Everything represented as graph nodes

The utterances gradually build the graph

Inference rules can further connect the nodes(or add new ones)

⇒ Representation of knowledge, usable for inferencing and QA



Conclusions




Conclusions


MultiNet Example: “The car was damaged because of the impact.”



Conclusions


MultiNet – technical info

Properties of MultiNet

93 relations + 18 functions

7 layers of attributes

hierarchy of 46 sorts

1 edge-end attribute distinguishing immanent (prototypical /categorical) vs. situational knowledge

encapsulation of concepts

default vs. categorical inference rules



Conclusions


Prague Dependency Treebank

Developed at the Institute of Formaland Applied Linguistics, CharlesUniversity, Prague

Three layers of annotation

3,168 documents ≈ 49,442 sentences≈ 833,357 tokens annotated on allthree layers.



Conclusions





Conclusions



Sed’ klidne, nehybej se, nabadala me prıtelkyne. Kritizovali hvezdny system, verıce v autenticnost dosudneokoukanych tvarı, ktere se vsak zahy take staly hvezdami (a nenı to jen osud Belmonduv). Pacient, vzpomenuv sina vsechna prıkorı zpusobena mu spolecnostı, vztahl na doktora ruku.



Conclusions



Sed’ klidne, nehybej se, nabadala me prıtelkyne. Kritizovali hvezdny system, verıce v autenticnost dosudneokoukanych tvarı, ktere se vsak zahy take staly hvezdami (a nenı to jen osud Belmonduv). Pacient, vzpomenuv sina vsechna prıkorı zpusobena mu spolecnostı, vztahl na doktora ruku.

.

.

t-lnd94103-085-p1s21B

root

přítelkyně

ACT

n.denot

#PersPron

ADDR

n.pron.def.pers

nabádat enunc

PRED

v

#PersPron

ACT

n.pron.def.pers

sedět

PAT

v

klidný

MANN

adj.denot

#Comma enunc

CONJ

coap

#Neg

RHEM

atom

hýbat_se

PAT

v

#PersPron

.

.

.

t-ln94200-173-p2s6

root

#PersPron

ACT

n.pron.def.pers

kritizovat enunc

PRED

v

systém

PAT

n.denot

hvězdný

RSTR

adj.denot

#Cor

ACT

qcomplex

věřit

COMPL

v

autentičnost

PAT

n.denot.neg

tvář

APP

n.denot

okoukaný

RSTR

adj.denot

dosud

TTILL

adv.denot.ngrad.nneg

však

PREC

atom

který

ACT

n.pron.indef

záhy

TWHEN basic

adv.denot.ngrad.nneg

také

RHEM

atom

stát_se

RSTR

v

hvězda

PAT

n.denot

a

PREC

atom

ten

ACT

n.pron.def.demon

#Neg

RHEM

atom

být enunc

PAR

v

osud

PAT

n.denot

jen

RHEM

atom

Belmondo

APP

n.denot

.

t-ln94211-120-p5s4

root

pacient

ACT

n.denot

#Cor

ACT

qcomplex

vzpomenout_si

COMPL

v

příkoří

PAT

n.denot

#PersPron

PAT

n.pron.def.pers

způsobený

RSTR

adj.denot

společnost

ACT

n.denot

který

RSTR

adj.pron.indef

vztáhnout enunc

PRED

v

doktor

PAT

n.denot

ruku

DPHR

dphr



Conclusions


Tectogrammatical Representation

Properties of Tectogrammatical Layer

One sentence ≈ one tree

Auxiliaries and function words removed

Missing obligatory valents inserted

Attributes of nodes

FunctorSemantic part of speech15 grammatemes (negation, tense, politeness, . . . )Topic-Focus distinctionSentential modality+ technical attributes (coordinations, parentheses, IDs)



Conclusions


Tectogrammatical Representation

.

.

t-lnd94103-085-p1s21B

root

přítelkyně

ACT

n.denot

#PersPron

ADDR

n.pron.def.pers

nabádat enunc

PRED

v

#PersPron

ACT

n.pron.def.pers

sedět

PAT

v

klidný

MANN

adj.denot

#Comma enunc

CONJ

coap

#Neg

RHEM

atom

hýbat_se

PAT

v



Conclusions


Additional Required Information

Missing Pieces

1 Named entities recognition

NumbersPlacesPeople. . .

2 Metadata

AuthorDatePlaceDocument typeIntended recipient of the textBibliographical and other references



Conclusions

MappingTopic-Focus ArticulationAdditional Requirements

Presentation Outline Again

1 MotivationMultiNet – Knowledge RepresentationPrague Dependency TreebankMissing pieces

2 Issues of transformationMappingTopic-Focus ArticulationAdditional Requirements

3 ConclusionsConclusionsRelated WorkFuture Work



Conclusions


Mapping of Representational Means

Main Issues of Transformation

– closer look

1 Mapping of edges and corresponding functors in TR toMultiNet cognitive roles

2 Mapping of TR nodes to MultiNet concepts

3 Mapping of various natural language constructs toattribute-value assignments

4 Mapping of verbal tenses to temporal axis



Conclusions



Main Issues of Transformation – closer look 1


Actor and Patient highly ambiguousLocation functors are used also where no location is involved(ELMT, CTXT, SITU)However, other functors correspond quite straightforwardly toMultiNet roles (a table is presented in the paper)






Conclusions






Typically, a TR node corresponds to a MultiNet concept (i.e.,also a node)Quite often, a TR node corresponds to a subnetwork inMultiNetSometimes, the TR node corresponds to an edge in MultiNet(e.g., CORR, CTXT)





Conclusions







The color of x is y .

x has y color.

x is y .

y is the color of x .

ATTR

SUB

color

VAL

y

x4 Mapping of verbal tenses to temporal axis



Conclusions








Verbal tenses encoded in grammatemesIn MultiNet, TEMP, ANTE, DUR, STRT, and FIN relations can beused.



Conclusions


Topic-Focus Articulation

TFA in PDT

TFA is annotated on the Tectogrammatical layer

Every word has an attribute: c, t, or f

The nodes are ordered with respect to“communicativedynamism”

⇓

TFA in MultiNet

Content expressed by TFA is further analyzed into:1 Encapsulation of concepts2 Scope of quantifiers3 Layer attributes (GENER, REFER, VARIA, . . . )



Conclusions


Additional Requirements

Additional Requirements

1 Spatio-Temporal Representation

For simple inferences about space and time

2 Calendar

For computations with dates

3 Ontology

For all kinds of inferencesOntology is an inherent part of MultiNet semantic networkdesignUpper conceptual ontology represented by sorts



Conclusions

ConclusionsRelated WorkFuture Work

Conclusions

Conclusions

MultiNet is a suitable formalism for inferences and QA

It’s difficult to transform texts into MultiNet

Tectogrammatical representation is not designed forinferencing and QA

There are tools for text-to-TR conversion

TR is a good starting point for conversion to MultiNet(structural similarity, disambiguation in TR)

We have presented issues arising in such a process



Conclusions


Related Work

Related Work

Helbig (1986): Automatical transformation to MultiNet

Horak (2001): Automatical transformation to TransparentIntensional Logic

Callmeier et al. (2004): DeepThought project – automaticaltransformation to Robust Minimal Recursion Semantics

Bos (2005): Automatical transformation to DiscourseRepresentation Theory

Bolshakov and Gelbukh (2000): Automatical transformationin Meaning–Text Theory framework

Kruijff-Korbayova (1998): TR to DRT automaticaltransformation



Conclusions


Future Work

Future Work

1 Stage I – Preparation

Annotation toolsAnnotation guidelines

2 Stage II – Annotation

Pilot studyAutomated preprocessingEvaluation of annotators

3 Stage III – Application

Supervised“parsing”Assessment of TR necessity


on distance between deep syntax and semantic representation

Technology