CONTROLLED ACTIVE PROCEDURES AS A TOOL FOR LINGUISTIC ENGINEERING 
Heinz-Dirk Luckhardt 
Manfred Thiel 
Sonderforschungsbereich i00 
"Elektronische Spraohforschung" 
Universitat des Saarlandes 
D-6600 Saarbr~cken ii 
Bundesrepublik Deutschland 
Abstract 
Controlled active procedures are productions 
that are grouped under and activated by units called 
'scouts'. Scouts are controlled by units called 
'missions', which also select relevant sections from 
the data structure for rule application. Following 
the problem reduction method, the parsing problem is 
subdivided into ever smaller subproblems, each one 
of which is represented by a mission. The elementary 
problems are represented by scouts. The CAP grammar 
formalism is based on experience gained with natural 
language (NL) analysis and translation by computer 
in the Sonderforschungsbereich I00 at the University 
of Saarbrdcken over the past twelve years and dic- 
tated by the wish to develop an efficient parser for 
random NL texts on a sound theoretical basis. The 
idea has ripened in discussions with colleagues from 
the EUROTRA-project and is based on what Heinz-Die- 
ter Maas has developed in the framework of the SUSY- 
II system. 
In the present paper, CAP is introduced as a 
means of linguistic engineering (cf. Simmons 1985), 
which covers aspects like rule writing, parsing 
strategies, syntactic and semantic representation of 
meaning, representation of lexical knowledge etc. 
Survey of some ideas behind CAP 
The data structure used in CAP is a type of chart 
called S-graph (see Maas 1985). Charts are used in 
parsing quite frequently (cf. Kay 1977, Varile 
1983). The S-graph is an acyclic directed graph with 
exactly one start node and one end node. Each arc 
carries non-structural information and may carry 
structural information that is also represented as 
an S-graph. The non-structural information is a set 
of property/value-pairs called 'decoration'. It in- 
cludes 
a) a morDhosvntactic t_vpe (MS), i.e. the terminal 
or non-terminal category 
b) a surface-syntactic function (SF) 
c) a~\]9_e~nt~ctic function (DSF) 
d) a semantic relation (SR) 
e) a W~i.C~ 
f) information specific to an MS 
The structure of the complex NP 'trouble with 
Max' is visible to the user as Fig. i. 
trouble NP 
with Max 
Fig. 1 
NP 
nl o ................ o n2 
\[ 
! N NP 
O ....... O ...... O 
trouble ! 
\[ PRP N 
o ..... o ...... o 
Fig. 2 with Max 
If we interpret the nodes as arcs, we receive the 
S-graph representation (Fig. 2). Hence, we shall use 
'node' and 'arc' as synonyms. The ambiguity of 
'trouble with Max' is represented by a sequence of 
two NP-arcs that also goes from nl to n2. 
Much like most modern grammar theories (LFG, GPSG 
etc.), the CAP-concept is based on context-free 
rules. CAP differs from these theories in the way 
well-known problems of cf-grammars are dealt with. 
Where GPSG employs meta-rules, derived categories, 
and the ID/LP-formalism, LFG uses different struc- 
tural concepts (C- and F-structures) and - above all 
- lexical knowledge. LFG and GPSG are augmented PS- 
grammars. With CAP PS-grammar has been abandoned. 
This is due to the fact that PS-grammars imply 
strict word order, and non-standard word order can 
only be handled by means of transformations (TG) or 
derived categories/new formalisms (GPSG). 
In principle, in CAP constituents are accepted 
where they are found in natural language utterances. 
It is assumed more natural to accept and represent 
the constituents 'wen' (whom) and 'du' (you) in 'Wen 
liebst du?' (Who(m) do you love?) in their respec- 
tive positions as accusative and nominative object 
than to mark the gaps in the representation where 
those constituents 'ought' to appear or to move them 
to their standard position and to leave a trace in 
the original \[.,lace. 
LFG and GPSG do not use transformations. In CAP 
transformations are possible, but they serve other 
purposes than in TG. They are not employed to ac- 
count for structures that are not covered by 
standard PS-rules (the ID/LP-formalism was invented 
for that reason). On the one hand, transformations 
serve to 'normalise' certain surface structures, in 
order to make rule writing easier (cf. Luckhardt 
1986). On the other hand, they produce the deep 
structure necessary for the disambiguation of lex- 
emes and for other purposes of machine translation, 
e.g. by re-introducing deleted complements. 
Unlike the government-and-binding theory, CAP 
moves constituents only in those cases, where this 
movement can be achieved without damage to represen- 
tation without leaving a trace. E.g. in '... lastet 
dem Angeklagten das Verbreohen an.' (... charges the 
defendant with the crime.) the verbal prefix 'an' is 
moved to the left of 'lastet', so that the correct 
frame (i.e. the frame of 'anlasten' which requires a 
new dictionary look-up) can be used for assigning 
syntactic functions to the complements. TG-typical 
transformations like passive transformations are not 
employed, as the equivalent can be achieved by sim- 
ple feature assignment. 
In all, the CAP-parser for German (CAP-G) that is 
currently being developed may be regarded as a 
strictly controlled production system, where rule 
464 
application is controlled in two respects: 
a) 'missions' have to fulfil certain linguistic 
tasks. They are organised hierarchically, so 
that the higher missions may be said to be de- 
composed into partial (simpler) tasks (cf. Fig. 
3). Thus the parsing strategy can be formulated 
quite explicitly. For every mission ~i 'expecta- 
tion' maybe formulated that allows it to select 
parts of the database that look 'promising' for 
the application of certain rules. The mode of 
application (see below) can be determined by the 
linguist. 
Fig. 3 NP-MISSION 
SIMPLE-NP COMPLEX-NP 
AP-MISSION N=>NPAI~NP=>NPA%TRIB~I~S COORDINATION 
b) If a linguistic task cannot be subdivided any 
further, a 'scout', that represents such an ele- 
mentary task, selects a path from the data 
structure offered, i.e. an unambiguous sequence 
of arcs, and tries to apply a rule or set of 
rules to this path. The grouping of rules into 
larger units has also been pro~msed by Carter- 
/Freiling 1984 and others. 
This way of organising rules safeguards that t1~ 
rule writer is relieved of looking at parallel 
structures. Rules can be simple, since feature 
agreement m6~ be checked in missions and scouts so 
that rules may be kept general enough to be used in 
different places, i.e. in different scouts. The 
linguist can be quite sure his rules are applied the 
way he wants t1~m to and to the structures intended. 
In fact, certain rules would be quite harmful, if 
they were allowed to operate on arbitrary struc- 
tures. Rules ought to be perspicuous, but we think 
they cannot always be as simple as theoretical 
linguists would like them to be. 
The application of cf-rules such as NP+PRED=>PRED 
may be subject to a number of restrictions. Earlier 
experience with SUSY has shown that X~ 
(cf. below) is a good basis for such a strategy, 
e.g.: 
PRED + NP1 => PRED (NPI) / condition: 
NPI may fill a slot 
in the valency frame of PRED 
After the application of such rules the corre- 
sponding valency is deleted; these rules are applied 
in parallel and by iteration. They are based on what 
Dowty (cf. Dowty 1982) calls the 'grammatical rela- 
tions principle'. 
CAP rules are augmented, i.e. they are not just 
structure-building rules like the ones above, but 
contain also conditions for their application, for- 
mulated for the left-hand side, and assignments to 
the symbols on the right-hand side (see below). This 
approach, of course, is not new and has been taken 
in METAL, PATR-II, LIFER, DIAGRAM, and many other 
systems. The way conditions and assignments are for- 
mulated is described below. 
CAP possesses strong lexical and morphological 
components. '£hese stem from its predecessor and are 
believed to be a prereouisite for efficient parsing 
rather than a part of the parsing J~ 
Dependency grammar offers a secure foundation for 
the arknlysis of free-word-order languages like Ger- 
man or Russian and by no means impedes the analysis 
of languages like English or French, as has already 
been demonstrated with the SUSY MT system in the 
70's (cf. Luckhardt/Maa~:~Thiel 1984)o Moreover, for 
the sake of easier rule writing, it is helpful to 
represent all arguments of a predicate as sister 
nodes of each other and as sister nodes of the pred- 
icate's governor. This approach supports frame- 
oriented linguistic procedures (e.g. for the anal- 
ysis of complements and complement clauses, trans- 
lation of valency-bound constituents etc.) in a 
direct way, whereas the representation of such phe- 
nomena is not so natural in a phrase structure nota- 
tion. 
Rules, scouts, and missions 
CAP rules, scouts, and missions are written in a 
functional metalanguage (FUSL, cf. Bauer et al. 
1986). There are five types of rules according to 
the effect they have: 
blending rule: A + B => C 
start rule: A => X (A) 
right ex~insion: A (X) + B => A (X + B) 
left expansion: A + B (X) => B (A + X) 
concatenati~\]: A + B => X (A + B) 
A blending rule may be employed where a constit- 
uent structure does not have to be preserved, as in: 
AUX+PTC => FIV for: 'was' + 'treated' => 
treat (TENSE=PAS~; MS=FINITE VERB, VOICE=PASS) 
AUX 4- INP => FIV for: 'will' + 'treat' => 
treat (~NSE=FUT etc. ) 
C 
! ! 
! ! 
! ! 
O ..... O ...... O => O ....... O ....... O 
A B A B Fig. 5 
The assignment part of such rules, of course, has 
to furnish the new arc on the right-hand side with 
the respectiw~ property/value pairs (cf. brackets)° 
The effect of A + B => C is demonstrated in l!qg. 5. 
The arcs A and B remain intact and may be used by 
other rules. Thus a quasi-parallel processing is 
guaranteed. In cases of non-aii~oiguous structures, A 
and B may be deleted explicitly in the scout that 
invokes the rule. 
! ! X ! 
: o .... o , 
! A' ! 
0 ........ 0 => 0 ............ 0 
A A Fig. 6 
A start rule is employed where a non-terminal arc 
is constructed from a terminal. A => X ( A ) means 
that a new arc X is produced with A as its substruc- 
ture which spans the same part of the data structure 
as does A, cf. Fig. 6. 
465 
An expansion rule adds an arc as a sister arc to 
the substructure X of another arc. A (X) + B => 
A (X + B) has as a result the structure represented 
in Fig. 7. 
Fig. 7 
O ........ O ........ O 
! A B 
! 
O ......... O 
X 
=> 
! ! A ! 
! ! ! 
: o- .... o ...... o ! 
! X B i 
o .......... o ....... o 
! A B 
! 
O ........ O 
X 
A + B (Z) => B (A + X) is employed analogously. 
Concatenation rules are used to express coordina- 
tion: 
NP + COMMA + NP = NEWNP (NP + COMMA + NP) 
N%~ + CONJ + NP = NEWNP (NP + CONJ + NP) 
These ru\].es produce deep structures. For 'Peter, 
Mary and John' the structure in Fig. 8 is generated. 
Fig. 8 
NEWNP 
NP COMMA NEWNP 
'Peter' NP CONJ NP I I 
'Mary' 'John' 
CAP rules have the architecture given in Fig. 9. 
r~\]le RULENAME 
lhs <left-hand side> 
conditions <restrictions on lhs> 
rhs <right-hand side> 
assigrm~ents <assignments to rhs> 
end Fig. 9 
The conditions part may be empty. It allows navi- 
gation in the processed subchart and a variety of 
restrictions by means of logical expressions. This 
is also true for the assignments part, which, how- 
ever, must be Don-empty. An example is given in 
Fig. 9a. 
rule PREO+SUBJ Fig. 9a 
lhs X + Y 
conditions eq (MS of X, PRED) 
eq (MS of Y, NP) 
notempty (int (FRAME of X, 
SCASE of Y, 
<NO~>) ) 
note.ioty (int (PERNUM of X, 
PERNUM of Y)) 
rhs Z ( subX + Y ) 
assignments copydec (Z, X) 
assign (SF of Y', SUBJECT) 
assign (FRAME of X, 
rain (FRAME of X, <NOM>) 
assign (SCASE of Y, <NOM>) 
end 
Two neighbouring arcs X and Y are expected, X 
being a PRED, Y an NP. The FRAME of X is to include 
NOMinative, which also has to be one of the cases of 
Y. The PERNUM feature structures for person and num- 
ber have to agree. The newly created arc Z that 
466 
covers the substructure of X plus the nounphrase Y 
inherits all property/value-pairs from X. The (sur- 
face-)syntactic function SUBJECT is assigned to the 
new arc Y' which is a copy of Y. The NOMinative-slot 
is deleted from the FRAME of X. Y is given the unam- 
biguous surface case NOMinative. 
The system of missions and scouts guarantees that 
PRED+SUBJ is invoked, when the chart consists of 
PREDs and NPs, i.e. when the SIMPLE-STRUCTURES-mis- 
sion has turned terminal elements into simple non- 
terminal ones (e.g. FIV=>PRED, DET+N=>NP etc.)° By 
iteration, the output of PRED+SUBJ is used to attach 
the rest of the complements (by rules like PRED+DAT, 
PRED+PRPOBJ,AKK+PREDetc.). 
Rules are grouped under and activated by what we 
call 'scouts'. A scout selects those paths (= unam- 
biguous sequences of arcs) from the S-graph to which 
the rules of the scout may be applied. The modes of 
application are: 
parallel: all rules are applied to the same structure 
stratificational: one rule is applied after the other 
(stop after failure 
preferential: stop after success 
iterat\]ve: repeat after success 
The architecture of scouts is given in Fig. i0. 
scout SCOUTNAME 
conditions 
< path with conditions on arcs > 
use ru\]e RULE1 
use rule RULEn 
params <mode of application> 
options <further options> 
end Fig. i0 
<path> is a sequence of normally not more than 
four arcs each of which is described in the <condi- 
tions on arcs> part (cf. Fig. 10a). 
conditions Fig. 10a 
arc 1 (X , mender (MS of X , 
<ART-DEF,ART-INDEF,DEM, POSP, IND>)) 
arc 2 (Y , equal (MS of Y , N)) 
Here two neighbouring arcs X and Y are described, 
'x' and 'Y' being names used only by this scout. The 
morphosyntactic category (MS) of X must be a men~oer 
of the set in angled brackets, the MS of Y must 
equal N. The scout selects all sequences ART-DEF + 
N, ART-INDEF + N etc. one after the other from the 
database offered by a mission (see below) and tries 
to apply its rules to them. The angled brackets 
enclose the set of determiner types that are thought 
to be relevant here (def. art., indef, art., dem. 
pronoun, poss. pronoun, indef, pronoun) and that may 
be combined with a noun to form an NP. Other scouts 
select paths like PREP + N, PREP + AP + N etc. They 
all have to be dealt with in different scouts, as 
the conditions for unifying them into an NP and the 
values the NP's inherit are quite different. 
Scouts are controlled by 'missions'. The system 
of rules, scouts, and missions presents the control 
structure of the parser (cf. example in Fig. 12). 
The elementary tasks of the parsing mission are or- 
ganised as scouts that activate those (sets of) 
rules that are to be applied to fulfil the intended 
task. The linguists are free to choose the strategy 
they like according to the field they intend to cov- 
er. The modes of application are the same as above. 
The architecture of missions is given in Fig. ii. 
mission MISSIONNAME Fig. ii 
expectations left-context 
scope <active area> 
right-context 
subproblems solve (subproblem i) 
solve (...) 
solve (subproblem n) 
parameters 
goal <goal structure> 
end 
mission PARSE-GERMAN Fig. 12 
mission SIMPLE-STR~S 
scout N=>NP 
rule N=>NP 
scout DET+ADJ+N=>NP 
rule ARTD+ADJ+N 
rule ARTI+ADJ+N 
rule POSP+AIXT+N 
mission COMPLEX~UCIIFRES 
n~ssion COMPLEX_NPS 
mission ATTRIBUTEs 
mission G EN I T IVE_ATI~R IB L~I~E 
... 
.,. 
end 
A mission consists of a list of submissions or 
scouts that are applied in the mode <mode>, if cer- 
tain 'expectations' (=preconditions) are fulfilled. 
The expectations part may be empty, so that the 
scouts may operate on the complete database. A well- 
defined structure may be formulated as the 'goal' of 
the mission. The expectations part describes a 
section of the S-graph where the scouts of that mis- 
sion may be successful, i.e. this section with all 
its ambiguities (= parallel arcs) is taken from the 
database and handed over to the scouts. An example 
is given in Fig. 13. 
expectations Fig. 13 
scope first (X , equal (MS of X , FIV)) 
mid (Y , member (MS of Y , <NP, AP>)) 
last (Z , equal (MS of Z , VERBPREFIX)) 
right-context (R , member (MS of R , 
<SEN, COMMA, NKO, SEM>) ) 
The part of the database between the nodes nl and 
n2 (cf. Fig. 14) is selected with all parallel 
structures, 'das Rauchen' being analysed as 'defi- 
nite article + noun' (in one NP) and as 'personal 
pronoun + noun' (in two NP's). The expectation is to 
be read as follows: The first arc must be marked 
'finite verb', the last one 'detached verbal pre- 
fix'. Between them one or more NP's and/or AP's (ad- 
jective phrases) in arbitrary distribution are ex- 
pected. A full stop, comma, coordinating conjunc- 
tion, or se./colon must be the right neighbour of Z, 
i.e. the arc left of n2. If these expectations are 
fulfilled, the partial S-graph that begins with X 
and ends with Z including all parallel arcs is acti- 
vated for the scouts of that mission. These expec- 
tations are so explicit, because in this way struc- 
tures may ~.~ disambiguated quite safely. In German, 
most verbal prefixes may also he prepositions, cf. 
(i) and (2). 
(i) Er gibt das Raucl~n ~. 
(He giw~s up smoking.) 
(2) Er gibt ein Konzert ~IL\[ der Gitarre. 
(He gives a concert on the guitar.) 
The expectations described exactly fit for (I), 
but not for (2), and the mission activates the data- 
base accordingly. 
I ! NP ! 
\[ o----o ......... O ! 
! ART N \[ 
nl o ....... o ...... o ............. o ....... o n2 
gibt ! das ! Rauchen ! auf 
! ! ! 
..................... 
!NP ! NP 
! ! 
o .... o o .......... o 
Fig. 14 PRDN N 
The scouts used for the analysis of detached 
verbal prefixes are the fol\]ow~\]g: 
solve RIGHT-EXPANSION 
solve PRE\[~VZ S 
The first scout increments the predicate in the 
partial database between nl and n2 until all NP's 
between the predicate and the verbal prefix are in 
the predicate's substructure, and the second scout 
concatenates verb and verbal prefix. The complete 
mission will look like Fig. 15. 
A different approach to this problem is 'normal- 
!sat!on' mentioned above, where the verbal prefix is 
moved to the finite verb in the first place. 
mission P~J~SE-VERBAL-PREFIXES: Fig. 15 
expectations 
scope first (X, equal (MS of X, FIV) 
mid (Y, member (MS of Y, <NP,AP>) 
last (Z, equal (MS of Z, VERBPREFIX) 
right--context (R, n~mber (MS of R, 
<SEN, COMMA, CONJ, SEM>) ) 
subproblen~ solve (RIGHT-EXPANSION) 
solve (PRED+VZ S) 
goal (G, equal (MS of G, PRED)) 
end 
Feature propagation 
When building syntactic structures, a parser 
transports features between nodes. In many modern 
grammar theories and formalisms this transport is 
achieved by unification (cf° Shieber 1985, Karttunen 
1984, Kay 1984). For a nulnber of reasons unification 
has no place in the CAP-concept (cf. Luckhardt 
1986a). Unification was introduced as a simple in- 
strument, which in fact has to achieve a very com- 
plex task. Feature propagation is too complex to be 
achieved by simple unification, and if the effect of 
unification is differentiated, it looses its ele- 
gance. 
In a rule like 'DET+ADJ+N=>NP' it has to be 
stated which features are inherited by the NP, i.e. 
ADJ and N may have a feature FRAME, but only that of 
467 
the N may be propagated. The same seems to be true 
for the semantic class. 
A difference has to be made between selective 
(FRAME) and inherent features (CASE). Karttunen 
(1984) gives an example where by unifying 'I 
(CASE=NOM)' and 'do' the feature CASE=NOM is inher- 
ited by the new predicate 'I do (CASE=NOM)' which is 
not really desirable. There are more cases where 
unification leads to undesirable feature propaga- 
tion. 
Especially in coordination features have to be 
matched explicitly which, perhaps, is not so obvious 
for English. The structures in Fig. 16 (out of the 
house and across the street) have to be unified 
without PCASE and CASE having to match. In Fig. \]7 
(from the conduct of Eva and her husband), however, 
the CASE-values have to match, in order to prevent 
the coordination of 'aus dem Verhalten' and 'ihres 
Marules', and PCASE=AUS is inherited by the new NP. 
aus dem Haus und fiber die Strag.e Fig. 16 
o ............... o ...... o ................... o 
PCASE=AUS PCASE=UBER 
CASE=DAT CASE=AKK 
aus dem Verhalten und ihres Mannes Fig. 17 
0 ..................... 0 ..... 0 .............. 0 
! PCASE=AUS CASE=GEN 
! CASE=DAT 
! Eras 
O ................... O 
C~E=GEN 
SF=GEN-ATI~ 
Only those features can be unified that are car- 
tied by at least one of the constituents, so that it 
is not easy to introduce features during the parsing 
mission, which is desirable in certain cases (cf. 
Luckbardt 1986a). On the other hand, it seems impos- 
sible to get rid of features that are no longer 
used, like the INFL-feature (after the agreement 
between the elements of an NP has been checked). 
In CAP, the effect of unification is achieved by 
an operation that consists of a test and an action 
using FUSL-functions like 
eq (NUMBER of X, ~ER of Y) 
int (FRAME of X, SCASE of Y) 
n~mber (MS of X, <ARTD,ARTI,POSP,DEM, IND> 
assign (SF of Y, SUBJECT) 
Thus explicit comparison, creation, deletion, and 
propagation of features is possible. 
Conclusion 
CAP has to be seen in the context of automatic 
analysis and translation of natural language. It 
commands a formalism that makes it suitable for the 
development of efficient parsers by allowing for ex- 
tensive means to represent linguistic knowledge 
strategies for its use. The way these aspects inter- 
act is currently being formalised by Thiel in his 
NLPT (Natural Language Processing Theory, cf. Thiel 
1985). 
The underlying data structure is the S-graph, 
which allows the management of all kinds of ambigui- 
468 
ties; moreover, the software system makes it unnec- 
essary for the linguist/user explicitly to take 
care of ambiguities. Thus he/she may write rules 
without worrying about parallel structures, as his- 
/her view of the data structure is a simple tree or 
sequence of trees. There are methods, however, for 
indicating preference to certain structures over 
others. 
Underlying linguistic features such as rule aug- 
mentation, feature propagation, lexicalisation etc. 
that are known from GPSG, FUG, LFG etc. have been 
extended to cover more phenomena, espec~ally those 
encountered when parsing German. They are used in a 
way that allows the analysis of random samples of 
text in comparably short time. 
Some special applications of CAP are 
-normalisation: removal of idiosyncrasies and 
treatment of constructions that are notorious for 
the problems they present (discontinous verb forms 
parentheses, etc.) 
- formalisation of the complex agreement conditions 
on Gern~n NP's, treatment of free word order 
- coping with complex forms of coordination 
- controlled inheritance of features 
- giving t|~ linguist the opportunity of determining 
the grade of featurisation and the depth of re- 
presentation 

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