DATR AS A LEXICAL COMPONENT FOR PATR 
James Kilbury, Petra Naerger, Ingrid Renz 
Seminar f/lr AUgemeine Sprachwissenschaft 
Heinrich-Heine-Universit,'tt Dilsseldorf 
Universitatsstrai\]e 1 
D-4000 l:Ydsseldorf 1 
Federal Republic of Germany 
e-mail: kilbury@dd0rud81 .bimet 
naerger@dd0rud81.bitnet 
renz@ dd0rud81 .bitnet 
ABSTRACT 
The representation of lexical entries 
requires special means which basic PATR sys- 
tems do not include. The language DATR, 
however, can be used to define an inheritance 
network serving as the lexical component. The 
integration of such a module into an existing 
PATR system leads to various problems which 
are discussed together with possible solutions 
in this paper. 
means that associated information is represented 
together or bundled. One advantage of this 
bundled information is its reusability, which 
allows redundancy to be reduced. The represen- 
tation of lexical information should enable us 
to express a further kind of generalization, 
namely the relations between regularity, sub- 
regularity, and irregularity. Furthermore, the 
representation has to be computationaUy tracta- 
ble and -- possibly with the addition of"syntac- 
tic sugar" -- more or less readable for human 
users. 
1 MOTIVATION 
In the project "Simulation of Lexical 
Acquisition" (SIMLEX) unification is used to 
create new lexical entries through the monoto- 
nic accumulation of contextual grammatical 
information during parsing. The system which 
we implemented for this purpose is a variant of 
PATR as described in (Shieber, 1986). 
Besides collecting the appropriate infor- 
marion for an unknown word, i.e. a lexeme not 
already specified in the given lexicon, the cre- 
ation of its new lexical entry is a major goal. 
In this context questions about the nature of 
lexical information, the structuring, and the 
representation of this information must be an- 
swered. The present paper is mainly concerned 
with the structuring and representation of infor- 
marion in lexical entries. 
2 REPRESENTATION OF LEXICAL 
INFORMATION 
The formalism of PATR offers two 
possible means of representing lexical informa- 
tion. First of all, the information can be encod- 
ed in feature structures directly. Except for 
computational tractability, none of the other 
criteria are met. The second facility consists of 
macros or templates which assemble the lin- 
gnistic information so that it can be reused in 
various places in the lexicon. This meets the 
most important of the above-mentioned condi- 
tions and reduces redundancy. But the encoded 
information is inherited monotonically, i.e. only 
regularities can be expressed. In order to struc- 
ture lexical information adequately, other rela- 
tions like subregularities and exceptions should 
also be expressible. 
Macros fail to achieve this, whereas 
default inheritance networks are well-suited for 
the purpose. In the following section we give 
an overview of one such network formalism 
which was primarily designed for representing 
lexical information. 
We assume that certain conditions must 
be met by an adequate representation of lexical 
information. The most important of these is that 
it captures linguistic generalizations, which 
- 137 - 
3 OVERVIEW OF DATR 
DATR (described in detail by Evans/ 
Gazdar, 1989a; 1989b; 1990)is a declarative 
language for the definition of semantic net- 
works which allows for defaults as well as 
multiple inheritance. Its general properties are 
non-monotonicity, functionality, and determinis- 
tic search. 
A DATR theory (or network descrip- 
tion) is a set of axthms (or expressions) which 
are related to each other by references. Togeth- 
er they define a hierarchical structure, a net. 
Both regularities and exceptions can be ex- 
pressed, regularities using default inheritance, 
and exceptions, overriding. 
DATR axioms consist of node-path 
pairs associated with a right-hand side. This 
can be a value (atomic or lis0, or an evaluable 
DATR expression if the value is to be inherit- 
ed from another node, path, or node-path pair. 
The following DATR theory comprising three 
node definitions I encodes familiar linguistic 
information to illustrate some relevant DATR 
features: 
(1) 
LEXIC.AL: <syn major bar> ~ zero. 
NOUN: <> == LEXICAL 
<syn major nv n> == yes 
<syn major nv v> == no. 
ADJ: o == LEXICAL 
<syn major nv n> == NOUN 
<syn major nv v> == 
<syn major nv n>. 
The represented information can be 
retrieved with special DATR queries. These 
also consist of a node-path pair, whose evalua- 
tion returns the value sought. With the above 
DATR description the following examples show 
sensible DATR queries and their corresponding 
values: 
(2) 
NOUN:<syn major nv n> 7 
yes (atomic value) 
NOUN:<syn major nv v> ? 
no (atomic value) 
NOUN:<syn major bar> ? 
zero (inherited from node LEXICAL) 
ADJ:<syn major nv n> ? 
yes (inherited from node NOUN) 
ADJ:<syn major nv v> ? 
yes (inherited from node NOUN via path 
<syn major nv n> in node ADJ) 
ADJ:<syn major bar> ? 
zero (inherited from node LEXICAL) 
Seven inference rules and a default 
mechanism are given for the evaluation of 
DATR queries. Their precise semantics and 
properties are described in (Evans/Gazdar, 
1989b; 1990). 
A major feature of DATR is its distinc- 
tion between global and local inheritance. In 
the above example only local inheritance is 
involved, but global inheritance plays a crucial 
role in one of the later examples. Variables 
constitute an additional device available in 
DATR but are assumed to have the status of 
abbreviations. 
Despite their syntactic similarities, 
DATR and PATR differ completely in their 
semantics, so that there is no obvious way of 
relating the two formalisms to each other. Some 
approaches are discussed in the next section. 
4 RELATING DATR AND PATR 
A PATR system needs to have the 
lexical information it uses encoded in feature 
structures consisting of attribute-value pairs. 
The lexical information represented in the 
DATR theory above (1) would appear as fol- 
lows when stated in feature structures: 
- 138 - 
(3) 
information specific to NO: 
syn.'  or. ~nv r; \["  '1/11 
tv: nOllll 
information specific to ADJO: ~ 
n: najor. 
r In 
The question that arises is how to relate 
DATR and PATR so that the hierarchically 
structured lexical information in DATR can be 
made available in PATR-usable feature struc- 
tures. 
4.1 A DATR-PATR INTERFACE 
The first idea that one might have is to 
exploit the syntactic similarities between the 
two formalisms and encode the lexical informa- 
tion in a DATR description like (1). In this way 
a DATR axiom like NOUN: <~yn major nv n> 
== yes would be directly equivalent to the path 
equation <NOUN syn major nv n> = yes in 
PATR, where the node name in DATR corre- 
sponds to the variable name for a feature struc- 
ture in PATR. Although this looks reasonable, 
one major problem arises: You must know 
exactly the path you want to query, i.e. all its 
attributes and their precise order. If such a 
query is posed, the answer will be the atomic 
value yielded by the DATR evaluation. 
Such an approach requires an interface 
with the following functions: Queries that the 
grammar writer has stated explicitly have to be 
passed on to DATR. Every query together with 
the resulthag value has to be transformed into 
a PATR path equation (that partially describes 
a feature structure) and passed on to the PATR 
system. What is most disturbing about this 
strategy is the fact that for every distinct PATR 
path you have to know the corresponding 
DATR query. It is tempting to think one could 
simply check which paths are defined for a 
given node, but this doesn't work because of 
inheritance: the entire network is potentially 
relevant. So in effect all the PATR structures 
except the atomic values have to be defined 
twice: once in the DATR statements and once 
in the queries. This redundancy cannot be elim- 
inated unless types for the feature structure are 
declared which are consulted in formulating the 
queries. 
4.2 USING DATR OUTPUT DIRECTLY 
A completely different approach is to 
formulate a DATR theory which gives the 
lexical information in a PATR-usable format 
(i.e. a feature structure) as the result of the 
evaluation of a DATR query. Thus, the DATR 
description reflects the hierarchical structure of 
the lexical information and consequently meet.~ 
one of the main requirements for an adequate 
representation that cannot be met by a simple 
PATR formalism. The resulting feature struc- 
tures include all the information necessary for 
PATR but neglect the inheritance structure, 
although the latter is involved in their construc- 
tion (i.e. the evaluation of queries). There are 
various DATR-programming techniques that 
realize these ideas. Three examples will b:: 
presented here which cover the lexical informa- 
tion encoded in (1). 
The first technique, which is illustrated 
in (4) 2 , uses global inheritance (represented 
with double quotation marks) to store the node 
at which the query originates. This also allows 
other information in the global node to be. 
accessed. 
- 139 - 
(4) 
SYNTAX: 
MAJOR: 
( maj ':' 
NV: 
( nv ':' \[ 
NOUN: 
ADJ: 
<> == ( \[ syn ':' \[ "<synpaths>" \] \] ). 
<> == SYNTAX 
<synpaths> == 
\[ "<tmj~ths>" \] ). 
<> == MAJOR 
<majpafils> == 
n ':' "<n>", v ':' "<v>" \]). 
o == NV 
<n> == yes 
~'W> == no. 
<> == NV 
<n> == yes 
<v> == yes. 
BAR: <> == MAJOR 
<majpaths> == ( bar ':' "<bar>" ). 
BAR0: o == BAR 
<bar> ~ zero. 
This DATR theory makes it possible to 
get the feature structure associated with the 
node NOUN, i.e. the evaluation of the DATR 
query NOUN:<>. 
To evaluate this DATR query the nodes 
NV, MAJOR, and SYNTAX are visited. In the 
node SYNTAX part of the corresponding feature 
specification is constructed and the evaluable 
path <synpaths> refers back to the original 
node NOUN. Then the query NOUN: 
<synpaths> is evaluated in the same way up to 
the node MAJOR, where the next part of the 
feature structure is built and the evaluable path 
<majpaths> refers again to the global node 
NOUN. At the end of the evaluation the feature 
structure \[syn:\[maj:\[nv: ln:yes,v:no1111 emer- 
ges. 
Lexical entries defined with the DATR 
network above have the form FROG: <> == 
("NOUN .... BARO"), which means intuitively 
that the lexeme frog is an nO. Given the net- 
work in (4), the value of the query FROG:<> 
will inherit the information of the global nodes 
NOUN and BARO. Thus, the global environ- 
ment is changed in the course of the evaluation. 
As a declarative language, DATR is 
independent of the procedural evaluation strate- 
gies embodied in particular DATR-implementa- 
tions. Nevertheless, DATR theories like (4) 
may themselves reflect different evaluation 
strategies (just as different search strategies 
may be implemented in pure PROLOG, inde- 
pendently of the particular PROLOG implemen- 
tation). 
The evaluation strategy in (4) can be 
described as top-down depth-first and is rather 
costly because of the cyclic returns to the glob- 
al nodes. A more efficient strategy is illustrated 
in (5). This DATR description embodies a 
breadth-first search and uses variables (desig- 
nated by the prefix $) instead of changing the 
global environment. 
(5) 
SYNTAX: <$NV $BAR> == 
( \[ syn ':' \[ MAJOR:<$NV $BAR> \] \] ). 
MAJOR: <$NV $BAR> == 
( maj ':' \[ NV:<$NV>, BAR:<$BAR> \] ). 
NV: 
N: 
V: 
N VAL: 
V VAL: 
( nv *" \[ 
<$NV> == 
N:<$NV>, V:<$NV> \] ). 
<$NV> == ( n ':' N_VAL:<$NV> ). 
<$NV> == ( v ':' V_VAL:<$NV> ). 
<noun> == yea 
<adj> ~ yes 
¢~ ~---~ 110. 
<verb> ~ yes 
<adj> ~ yes 
~ -~--= no. 
BAR: <$BAR> 
( bar ':' BAR_VAL:<$BAR> ). 
BAR_VAL: <barO> == zero 
<barl>  = ono 
<bar2> =ffi two. 
Here an appropriate query would be 
SYNTAX: <noun barO>. At the origin of the 
query the outer layer of the feature structure is 
already constructed. The rest of the feature 
structure results from evaluating MAJOR:<$NV 
$BAR>, where SNV is instantiated with noun 
and $BAR with barO as in the original query. 
We then obtain the feature structure 
\[syn:\[maj:\[nv:\[n:yes,v:no\],bar:zero\]\]\] as the 
result of the evaluation. Unlike the network in 
(4), it is not possible to ask for just a part of 
this feature structure: Neither the information 
about the N/V-scheme nor the information 
about the bar level can be queried separately. 
An entry for the lexeme frog given the 
network (5) would have the form FROG:<> 
== SYNTAX::<noun barO>, which of .course 
also means that the lexeme frog is an nO. But 
this time the information is inherited from the 
- 140 - 
node SYNTAX, where the value provides the 
frame for the resulting PATR feature structure. 
Apart from the differing DATR tech- 
niques employed, the resulting feature struc- 
tures for a lexical entry also differ slightly. 
While the first is nearer to a set of PATR paths 
which has to be collapsed into a single feature 
structure, the second has exactly the form re- 
quired by the PATR system we use. 
The third technique is illustrated in (6). 
(6) 
SYNTAX: 
MAJOR: 
NV: 
BAR: 
N: 
V: 
<> == ( syn ':' \[ MAJOR \] ). 
<> == (maj ':' \[ NV, BAR 1). 
<> == (nv ':' \[N,V\]). 
<> == ( bar ':' "<bar>" ). 
== ( n ':' <value "<eat>"> ) 
<value nO'> == yes 
<value adj0> ~ yes 
<value> =--- rio. 
0 == ( v ':' <value "<cat>"> ) 
<value vO> == yes 
<value adjO> == yes 
<value> == no. 
LEXICAL: ~:> == ( \[ SYNTAX \] ) 
<bar> ~ zero. 
NOUN: <> == LEXICAL 
<Cat> == riO. 
ADJ: <> == LEXICAL 
<cat> == adjO. 
An appropriate query for this DATR 
theory would be NOUN:<>, the value of which 
is \[syn:lmaj:\[nv:\[n:yes,v:no1,bar:zero111. The 
evaluation of this query is similar to the one in 
(5) in that the value of SYNTAX:<> constitutes 
the frame of the resulting PATR-usable feature 
structure. Unlike (5), no variables are used; 
instead, information from the global node is 
used via global path inheritance to specify the 
values. Notice that whereas with (4) the global 
node is changed, it remains unchanged during 
the evaluations with (6). 
The advantages of (6) are obvious. 
Since neither variables nor global nodes are 
used, fewer DATR facilities are involved. Nev- 
ertheless, the required PATR feature structures 
Can be defined. For example, the lexical entry 
for frog would be FROG:<>==NOUN, where 
the noun-specific information is inherited from 
NOUN. 
This third approach forms the base for 
our current lexicon. Some of the related issues 
are raised in the next section. 
5 THE DATR LEXICON 
It has been shown above that DATR 
theories can serve as a lexicon for a PAT R 
system where the lexemes are represented as 
DATR nodes and the returned values of queries 
are the corresponding feature structures. In a 
lexicon which is formulated as in (6), aparl; 
from the lexical nodes (i.e. nodes like FROG 
which define lexemes) two other kinds of nodes 
can be distinguished: nodes like SYNTAX or 
NV, which correspond to PATR attributes, and 
nodes like NOUN or LEX/CAL, which represent 
a kind of type information (see Pollard/Sag, 
1987). The lexemes inherit this information 
through reference to the type nodes, while the 
lexeme-specific information is as~ciated direct. 
ly with the lexical nodes. 
There are several differences between 
these three kinds of nodes. Whereas it is appro. 
priate to pose a query like FROG:<> or 
NOUN:<>, such queries make no sense for 
nodes like SYNTAX. In this respect lexemes and 
types are related. 
Another property distinguishes lexical 
nodes from type nodes. The latter are hierarchi- 
cally structured, while the former are unstruc- 
tured in the sense that they refer to types but 
not to other lexemes. The structuring of the 
type nodes reflects the above mentioned regu- 
larities as well as irregularities. 
The following DATR theory is a lexi- 
con fragment for a possible classification of 
intransitive verbs in German. Regular verbs 
(e.g. schlafen ',sleep') take a nominative subject 
and inherit all type-specific information from 
the node INTRANS_VERB. One exception are 
verbs with expletive subject (e.g. regnen 'rain'), 
another those with nonnominative (accusative 
or dative) subject (e.g. dilrsten 'suffer from 
thirst' with accusative). These verbs refer to the 
types nodes INTRANS_VERB_EXPL and IN- 
TRANS_VERB_ACC, respectively. The latter 
types inherit from the node INTRANS_VERB 
but override some of its information. 
141 - 
(7) INTRANS_VERB: 
INTRANS_VERB_EXPL: 
INTRANS_VERB_ACC: 
<> == VERB 
<cat subject> =ffi n2 
<case subject> ~ nm~a~e 
<status subject> ~ norm. 
== INTRANS_VERB 
<status subject> ~ expletive. 
<> == INTRANS VERB 
<case subject> ~ accusative. 
6 CONCLUDING REMARKS 
We have seen that it is possible to 
formulate the lexicon of a PATR system as a 
DATR theory. That is, given a lexical entry in 
DATR, a corresponding feature structure can be 
derived. A system postulating new entries for 
unknown words on the basis of contextual 
information during parsing (Kilbury, 1990) 
must be able to convert a given feature struc- 
ture into a corresponding lexical entry in DATR 
so that the new lexeme is located and integrated 
in the lexical network. To solve this problem 
the concept of type nodes can be exploited. 
A final difficulty involves certain 
PATR-specific devices like disjunctions and 
reentrancies for which no obvious DATR facili- 
ties are available. At present we still have only 
ad hoc solutions to these problems. 
FOOTNOTES 
1. NOUN: 
abbreviates 
NOUN: 
NOUN: 
<> == LEXICAL 
<syn major nv n> == yes. 
== LEXICAL 
<syn major nv n> == yes. 
2. The colons in single quotes, the commas, and the square 
brackets are DATR atoms, not part of the language itself.In 
contrast, the parentheses of DATR enclose a list value. 
ACKNOWLEDGEMENTS 
The research project SLMLEX is supported by 
the DFG under grant number Ki 374/1. The 
authors are indebted to the participants of the 
Workshop on Inheritance, Tilburg 1990. 
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