On Distance between Deep Syntax and Semantic Representation

Motivation
Issues of transformation
Conclusions

On Distance between Deep Syntax and Semantic
Representation

V´clav Nov´k
a a

Institute of Formal and Applied Linguistics
Charles University
Prague, Czech Republic

Frontiers in Linguistically Annotated Corpora
July 22, 2006, 16:00 – 16:30
Sydney, Australia

novak@ufal.mﬀ.cuni.cz Syntax – Semantic Distance 1/ 20

Motivation
Conclusions


Motivation
Conclusions

Presentation Outline

1 Motivation
MultiNet – Knowledge Representation
Prague Dependency Treebank
Missing pieces
2 Issues of transformation
Mapping
Topic-Focus Articulation
Additional Requirements
3 Conclusions
Conclusions
Related Work
Future Work


Motivation MultiNet – Knowledge Representation
Issues of transformation Prague Dependency Treebank
Conclusions Missing 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



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



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



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




Developed at the Institute of Formal
and Applied Linguistics, Charles
University, Prague
Three layers of annotation
3,168 documents ≈ 49,442 sentences
≈ 833,357 tokens annotated on all
three layers.



Sed’ klidnˇ, neh´bej se, nab´dala mˇ pˇıtelkynˇ. Kritizovali hvˇzdn´ syst´m, vˇˇıce v autentiˇnost dosud
e y a e r´ e e y e er´ c
neokoukan´ch tv´ˇı, kter´ se vˇak z´hy tak´ staly hvˇzdami (a nen´ to jen osud Belmond˚v). Pacient, vzpomenuv si
y ar´ e s a e e ı u
na vˇechna pˇıkoˇı zp˚soben´ mu spoleˇnost´ vzt´hl na doktora ruku.
s r´ r´ u a c ı, a




Sed’ klidnˇ, neh´bej se, nab´dala mˇ pˇıtelkynˇ. Kritizovali hvˇzdn´ syst´m, vˇˇıce v autentiˇnost dosud
e y a e r´ e e y e er´ c
neokoukan´ch tv´ˇı, kter´ se vˇak z´hy tak´ staly hvˇzdami (a nen´ to jen osud Belmond˚v). Pacient, vzpomenuv si
y ar´ e s a e e ı u
na vˇechna pˇıkoˇı zp˚soben´ mu spoleˇnost´ vzt´hl na doktora ruku.
s r´ r´ u a c ı, a
t-lnd94103-085-p1s21B t-ln94200-173-p2s6 t-ln94211-120-p5s4
root root root

nabádat.enunc kritizovat.enunc vztáhnout.enunc
PRED PRED PRED
v v v

přítelkyně #PersPron #Comma.enunc #PersPron #PersPron systém věřit pacient vzpomenout_si doktor ruku
ACT ADDR CONJ ACT PAT COMPL ACT COMPL PAT DPHR
n.denot n.pron.def.pers coap n.pron.def.pers n.denot v n.denot v n.denot dphr

#PersPron sedět hýbat_se hvězdný #Cor autentičnost #Cor příkoří
ACT PAT PAT RSTR ACT PAT ACT PAT
n.pron.def.pers v v adj.denot qcomplex n.denot.neg qcomplex n.denot

klidný #Neg tvář způsobený který
MANN RHEM APP RSTR RSTR
adj.denot atom n.denot adj.denot adj.pron.indef

okoukaný stát_se #PersPron společnost
RSTR RSTR PAT ACT
adj.denot v n.pron.def.pers n.denot

dosud však který záhy také hvězda být.enunc
TTILL PREC ACT TWHEN.basic RHEM PAT PAR
adv.denot.ngrad.nneg atom n.pron.indef adv.denot.ngrad.nneg atom n.denot v

a ten #Neg osud
PREC ACT RHEM PAT
atom n.pron.def.demon atom n.denot

jen Belmondo
RHEM APP
atom n.denot



Tectogrammatical Representation

Properties of Tectogrammatical Layer
One sentence ≈ one tree
Auxiliaries and function words removed
Missing obligatory valents inserted
Attributes of nodes
Functor
Semantic part of speech
15 grammatemes (negation, tense, politeness, . . . )
Topic-Focus distinction
Sentential modality
+ technical attributes (coordinations, parentheses, IDs)



Tectogrammatical Representation
t-lnd94103-085-p1s21B
root

nabádat.enunc
PRED
v

přítelkyně #PersPron #Comma.enunc
ACT ADDR CONJ
n.denot n.pron.def.pers coap

#PersPron sedět hýbat_se
ACT PAT PAT
n.pron.def.pers v v

klidný #Neg
MANN RHEM
adj.denot atom


Additional Required Information

Missing Pieces
1 Named entities recognition
Numbers
Places
People
...
2 Metadata
Author
Date
Place
Document type
Intended recipient of the text
Bibliographical and other references


Motivation Mapping
Issues of transformation Topic-Focus Articulation
Conclusions Additional Requirements

Presentation Outline Again

1 Motivation
MultiNet – Knowledge Representation
Missing pieces
2 Issues of transformation
Mapping
3 Conclusions
Conclusions
Related Work
Future Work


Motivation Mapping

Mapping of Representational Means

Main Issues of Transformation
1 Mapping of edges and corresponding functors in TR to

MultiNet cognitive roles
2 Mapping of TR nodes to MultiNet concepts
3 Mapping of various natural language constructs to
attribute-value assignments
4 Mapping of verbal tenses to temporal axis


Motivation Mapping


Main Issues of Transformation – closer look 1

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


Motivation Mapping




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


Motivation Mapping



attribute-value assignments color
The color of x is y .
SUB y
x has y color.
TR VAL
x is y . AT
y is the color of x. x


Motivation Mapping



Verbal tenses encoded in grammatemes
In MultiNet, TEMP, ANTE, DUR, STRT, and FIN relations can be
used.


Motivation Mapping

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 “communicative
dynamism”

⇓

TFA in MultiNet
Content expressed by TFA is further analyzed into:
1 Encapsulation of concepts
2 Scope of quantiﬁers
3 Layer attributes (GENER, REFER, VARIA, . . . )


Motivation Mapping


1 Spatio-Temporal Representation
For simple inferences about space and time
2 Calendar
For computations with dates
3 Ontology
For all kinds of inferences
Ontology is an inherent part of MultiNet semantic network
design
Upper conceptual ontology represented by sorts


Motivation Conclusions
Issues of transformation Related Work
Conclusions Future 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 for
inferencing 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



Related Work

Related Work
Helbig (1986): Automatical transformation to MultiNet
Hor´k (2001): Automatical transformation to Transparent
a
Intensional Logic
Callmeier et al. (2004): DeepThought project – automatical
transformation to Robust Minimal Recursion Semantics
Bos (2005): Automatical transformation to Discourse
Representation Theory
Bolshakov and Gelbukh (2000): Automatical transformation
in Meaning–Text Theory framework
Kruijﬀ-Korbayov´ (1998): TR to DRT automatical
a
transformation



Future Work

Future Work
1 Stage I – Preparation

Annotation tools
Annotation guidelines
2 Stage II – Annotation
Pilot study
Automated preprocessing
Evaluation of annotators
3 Stage III – Application
Supervised “parsing”
Assessment of TR necessity


On Distance between Deep Syntax and Semantic Representation

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