DECLARATIVE NOOEL FOR DEPENDENCY PARSING - 
k VIEV INTO BLACKBOARD METHOOOLOGY 
Vatkonen, K., Jippinen, H., Lehtota, A. and Ytltammi, N. 
KIELIKOHE-pr~ject, SITRA Foundation 
P.O.Box 329, SF-00121Hetsinki 
FinLand 
teL. inti + 358 0 641 877 
ABSTRACT 
This paper presents a declarative, dependency 
constraint model for parsing an infLectionaL free word 
order Language, t|ke Finnish. The structure of 
Finnish sentences is described as partial dependency 
trees of depth one. Parsing becomes a 
nondeterministtc search problem in the forest of 
partial parse trees. The search process is able to 
solve also ambiguities and tong-distance dependencies. 
Parsing is controLLed by a blackboard system. A 
~orking parser for Finnish has been implemented based 
on the modeL. 
1 |RTROOUCT|OB 
The development of our computational model for 
dependency parsing has progressed in three parallel 
and interrelated phases: 
(1) The development of a perspicuous high Level 
grammar specification Language which grasps well 
regularities and idiosyncracies of inflectional free 
word order Languages. 
(2) The acquisition of a grammar using that Language 
as the description media. 
(3) The development of a parsing strategy and a 
compiler for the specification Language. 
In our first approach the parsing process is described 
as a sequence of tocat decisions (Netimarkka et at. 
1984). A pair of adjacent structures of an input 
sentence is connected if there exists a valid binary 
dependency relation between them. Binary relations 
are boolean expressions of the morphological and 
syntactic restrictions on argument structures. In 
that first version dependency sructures were modelled 
procedurally with finite two-way automata (Lehtota et 
at. 1985). Grammar descriptions turned out to be 
complicated to handle, and due to purely Local 
decisions some gtobat phenomena, such as tong-distance 
dependencies, were not analyzed. 
A new grammar description formalism and computational 
method was developed: a declarative high Level 
Language FUMDPL (J~ppinen et at. 1986) for a grammar, 
and an underlying blackboard-based parsing method 
(Vatkonen and Lehtota, 1986). Recently, we have 
augmented the dependency parsing model to cover also 
tong-distance dependencies. According to the 
augmented model we have implemented a blackboard-based 
dependency parser ADP (Augmented Dependency Parser). 
In this paper we shortly describe our model and focus 
on the parsing strategy. For the grammar development 
environment and the compilation of the high \[ever 
description Language• see Lehtota et at. (1985 e 
1986). 
Our parsing method belongs to the class of constraint 
systems: a user specifies the constraints holding in 
the problem domain, and a goat for the computation. 
The interpreter must search for the goal. The result 
follows indirectly from the search process, in our 
model binary relations specify constraints on argument 
structures. The goal is to find a matching Local 
environment description for each word of an input 
sentence. As a side effect of the recognition 
corresponding partial dependency trees are built. The 
partial dependency trees ere Linked into a parse tree 
covering the whole sentence (Figure 1). 
PROOLEM SPAC(: p|rtt•l dependency trluu 
of depth one 
GOAL: • complete 
por•l tree 
W4 
~L 6/~o w5 
Sentence 
W I W z W 3 W 4 .. W n 
Figure 1. Parsing as a search process in a forest of 
partial dependency trees. 
218 
2 GRANNAR OESCR|PTION 
For the development of a grammar notation 
idiosyncracies of the object Language had to be 
observed. Finnish is a relatively free word order 
language. The syntactic-semantic knowledge ts often 
expressed in the inflections of the words. 
Furthermore, the parser was needed to work as a 
practical toot for real production applications, so 
the process of parsing was taken as a starting point 
instead of sentence generation. 
A grammar description consists of four parts: 
(1) Type definitions: Linguistic properties, features 
and categories. 
(2) A lexicon for associating features with words. 
(3) Binary dependency relations that may hold between 
regents and their dependents. 
(4) Functional 8¢hemmta for defining the Local 
environments of regents. 
2.1 Type definitions 
in the type definition part a grammar writer defines 
the types and their values used in a grammar 
description. This corresponds to the classification 
of Linguistic properties. There are three kinds of 
types: CATEGORIES, FEATURES end PROPERTIES. In 
addition to this the structure of the texical entries 
is described in this part. 
CATEGORY statement assigns names in hierarchies. For 
example, a category SyntCat for word classes could be 
defined as 
(CATEGORY: SyntCat 
< (Word) 
(Noun I Word) 
(Proper I Noun) 
(Common I Noun) 
(Pronoun I Word) 
(PersPron I Pron) 
(OemPron I Pron) 
(;ntPron I Pron) 
In a FEATURE statement • feature name and tts values 
are defined. Values can be mutuaLLy exclusive: 
adding of the complement value automaticaLLy destroys 
the old value. 
(FEATURE: SyntFeet 
< (Locative) ;a name of a place 
(InfAttr) ;a noun, that may have an 
tnfinittvial attribute 
(CountMessure) ;a countable measure noun 
°e. 
PROPERTY values are Like FEATURES except that they may 
have default values. For example: 
(PROPERTY: Polar < ( Pos ) Neg >) 
In this type definition polarity is positive by 
default. 
2.2 Lexicon 
The parser is preceded by a morphoLogicaL analyzer 
(J~ppinen and Ytitammi 1986). The morphological 
anatyzer produces for each word its morphological 
interpretation including texicat information. The 
parser associates default features for words. Those 
words which have idiosyncratic features, ms all verbs 
do, are in the parser~s Lexicon. Some example entries 
of the parser's lexicon: 
NETRi (Common (SubstNeasure)) 
HELSINKI (Proper (Locative)) 
AJATELLA (TrProcV (InfObj PsntisObj)) 
"Netri" (meter) is s measure unit for common nouns. 
"Netsink{" ts s proper noun and a name of a place. 
"Ajatetla" (to think) Js a transitive verb that may 
have infinittvtat or participle objects. 
2.3 Binary dependency retations 
The dependency parsing model aims at providing 
analyzed sentences with their dependency trees. 
According to this approach two elements of • sentence 
are directly related in a dependency relation tf one 
depends on another. The two elements ere catted the 
regent R (or head or governer) and the dependent 0 (or 
modifier). Binary relations define all permitted 
dependency relations that may exist between two words 
in Finnish sentences. For example, the binary 
relation Subject is the following boolean expression 
of the morphological end syntactic features of a 
finite verb and its nominal subject: 
(RELATION: Subject (D := Subject) 
((R = Verb Act 
(< lad Cond Imper Pot Ilpartis > (PersonP O)(PersonN D) 
- Negative - Auxiliary) 
(Auxiliary llpertis Nom - Negative) 
(Negative • limper Pr • (S 2P) Neg >) 
(Cond Pr S 3P) (Pot Pr Neg) 
(IIpartis Nom)> - Auxiliary)>) 
(D • PersPron Nom))... 
R must be an active verb. Further restrictions for it 
219 
appear within angle brackets that indicates a 
disjunction. Negation is expressed by "-". (PersonP 
D) (PersonH D) indicates an agreement test. O must be 
e personal pronoun in nominative case in this 
fragment. 
In our computational model words of an input sentence 
appear as complexes of their morphological, 
syntactical end semantic properties. We call this 
complex a constituent. If • binary relation holds 
between R and D, they ere adjoined into a single 
constituent. This ts what we mean by a functional 
description. It can be stated formally as mopping 
f(R,D) -> R I 
where R' stands for the regent R after that it has 
bound D. Function f is defined by the corresponding 
binary relation. This function abstraction should be 
distinguished from grammatical functions, even though 
in our grammar specification dependency relations also 
estimate grammatical functions. 
2.4 Functional schemata 
In functional schemata the Local environment of a 
regent is described by dependency functions. 
Functional schemata can be seen as partial dependency 
tree descriptions. A simplified schema for verb 
phrases, when a regent is • transitive verb and it is 
preceeded by s negative auxiliary verb, could be 
defined aS 
(SCHEHA: NegTronsVerb 
WHEN (AND (R • ProcVerb Act Transitive) 
(LEFT • Auxiliary Negative)) 
FUNCTIONS (NULTIPLE Adverbial) 
(OBLIGATORY Negation Subject Object) 
(LEFT Negation Subject Object Adverbial) 
(RIGHT Object Subject Adverbial) 
HARK (R := VerbP)) 
This scheme is able to recognize end build, for 
instance, pertlet dependency trees shown in Figure 2. 
¥mr~ WMrt~ Ver~ 
svb~ eeg eb} subl nag e~v e~| eaj s~j mql 
Figure 2. Example trees built by a schema NegTransVerb. 
There ere three parts in the simplified schema 
NegTransVerb: WHEN. FUMCTIOIIS end HARK. WHEN pert 
describes features for the regent and its context. 
FUNCTIONS part describes the dependents for the 
regent. NULT|PLE clause indicates which dependents 
may exist multiple times. OBLIGATORY names obligatory 
dependents. LEFT end RIGHT give the structure of the 
left and right context of the regent. 
The free word order is allowed by default because of 
the particular interpretation of the clauses LEFT and 
RIGHT. The definition only indicates which dependents 
exist in the named context, not their mutual order. 
ALl the permutations ere attoued. There is also means 
of fixing yard ordering. ORDER clause indicates 
mutual ordering of dependents. For example, a grammar 
writer may define for the simple NP#s 
(ORDER AdjAttr GenAttr R RelAttr) 
For this particular regent the most immediate Left 
netghbour must be a genetive attribute. The next to 
that is an adjective attribute. The right netghbour 
is a relative clause. 
For tong-distmnce dependencies the Local decision 
strategy must be augmented. The binding of 
|ong-dJstance dependents has two phases: the 
recognition end the actual binding. 
In transformational grammar, tong-distance 
dependencies ere dealt with by assuming that in the 
deep structure the missing word is in the place it 
would be in the corresponding simple sentence. It is 
then moved or deleted by a transformation. The 
essential point is that tong-distance dependency is 
caused by an element which has moved from the Local 
environment of • regent to the Local environment of 
another regent. Hence a moved element must be 
recognized by the functional schema associated with 
that Latter regent. The binding, then, is done Later 
on by the schema of the former regent. 
In the recognition phase the tong-distance dependents 
are recognized and bound "sway" (captured), so that 
the current regent can govern its environment. 
After this capture the possible Long-distance 
dependent remains waiting for binding by another 
scheme. 
Capturing dependency functions are marked tn the 
CAPTURE clause: 
(CAPTURE DistantNember) 
The dependency function DistentNember is general 
enough to capture all possible tong-distant 
dependents. For the actual binding of tong-distance 
dependents, one must mark in the clause DISTANT the 
dependents which may be distant: 
(DISTANT Object) 
220 
3 BLACKBOARD-BASED CONTROL FOB DEPENDENCY PARSING 
BLackboard ts a problem-solving model for expert 
systems (Hayes-Both et at. 1983, Nii 1986). We have 
adopted that concept end utilized it for parsing 
purposes. Our blackboard model application is rather 
simple (Figure 3). 
There are three main components: • blackboard, m 
control part end knowledge sources. The blackboard 
contains the active environment description for a 
regent. According to the structural knowledge in that 
environment description corresponding partial parse 
tree is built in the blackboard. Also all other 
changes in the state of computation are marked in the 
blackboard. 
Functional schemata and binary dependency relations 
are independent and separate knowledge sources; no 
communication happens between them. Art data flow 
takes place through the blackboard. Which module of 
knowledge to appty is determined dynemicalty, one step 
at • time, resulting in the incremental generation of 
partial solutions. 
In functional schemata s grammar writer has described 
Local environments for regents by dependency 
functions. The schemata are compiled into an internal 
LXSP-form. At s time, only one of the schemata is 
chosen as an active environment description for the 
current regent. The activated schema is matched with 
the environment of the regent by binary relation 
tests. The binary relations respond to the changes in 
the blackboard according to the structural description 
in the active schema and the properties of the regent 
and dependent candidates. At the same the partial 
dependency tree is built by corresponding dependency 
function applications. When s schema has been fully 
matched end the active regent bound to its dependents 
through function Links, the Local partial dependency 
parse is complete. 
A scheduler for knowledge sources controls the whole 
system. It monitors the changes on the blackboard and 
decides Mhat actions to take next. The scheduler 
employs • finite two-way automaton for recognition of 
the dependents. 
I 
BLACKBOARD KNOWLEDGE 
SOURCES 
,...,,v. i i,._.x.i environment description Functional schemata 
Partial solutions (local dependenc U 
trees) 
.... I~ -"*l dependencu 
Other computational state uoLa --I relations 
-- oon, o, ,,0 0o,o \] 
CONTROL 
figure 3. A blackboard model for dependency parsing. 
221 
3.1 The blackboard-based control strategy for 
dependency parsing 
For the format definition of the parsing process we 
describe the input sentence as a sequence 
(c(1),c(2),...,c(i-1), c(i), c(i+l),...,c(n)) of word 
constituents. With each constituent c(i) there is 
associated a set (s(i,1),...,s(i,m)) of functional 
schemata. The general parsing strategy for each word 
constituent c(t) can be modelled using • transition 
network. During parsing there ere five possible 
computational states for each constituent c(i): 
Sl The initial state. One of the schemata 
associated with ctt) is activated. 
S2 Left dependent• ere searched for c(i). 
$3 c(i) is waiting for the building of the right 
context. 
1) A schema candidate s(iek) associated with c(t) is 
activated, i.e. the constituent c(t) take• the rote 
of a regent. Following the environment description in 
s(i,k), dependents for c(i) are searched from its 
immediate neighbourhood. Go to the step 2 with j • 
i-1. 
2) The search of left dependents. There are two 
subcases: 
2a) There are no left neighbours (j = 0), none is 
expected for c(i), or c(j) (j < i) exists and is 
in the •tats $3. 
Go to the step 3 with j = j+l. 
2b) c(j) (j x i) exists and is in the state SS. 
Binary relation tests are done. In the case o? a 
• ucces the lipping f(c(i), c(j)) -> c(i)' takes 
place. Repeat the •tap 2 with j - j-1 end c(i) = 
cti),. 
S4 
S5 
Right dependent• are searched for c(i). 
The final state. The schema associated with c(i) 
has been fully matched and becomes inactive, c(i) 
is the head of the completed (partial) dependency 
tree. 
At any time, only one schema is active, i.e. only one 
constituent c(i) may be in the state B2 or S4. Only s 
completed constituent (one in the •tale S5) is allowed 
to be bound as s dependent for • regent. There may be 
s number of constituents simultaneously in the state 
S3. We call these pending constituent• (implemented 
as a •tack PENDING). 
3) Building the right context of the regent. There 
are two subcases: 
3a) There ere no right neighbours (j • n) or none 
is expected for c(i). Go to the •tap 5. 
3b) c(j) (j • i) exists. Go to the step 1 with 
c(i) : c(i+l) and PENDING = push (c(i), PEND%MG). 
4) The search of right dependents. Binary relation 
tests are done. in the case of succes the mapping 
f(c(i), c(j)) -> c(i) ~ takes place. Repeat the step 3 
with j = j+l and c(i) = c(i)'. 
5) The final state. There are two subcases: 
The parsing process •tarts with c(1) •nd proceeds to 
the right. Initially all constituents c(1),..,c(n) 
are in the •tats el. A sentence is welt formed if in 
the end of the parsing process the result i• • •ingle 
constituent that has reached the state S$ and contain• 
all other constituents bound In it• dependency tree. 
For each constituent c(i) the parsing process can be 
described by the following five steps. Parsing begins 
from the •tap 1 with i,k = 1. 
5a) The environment description has been matched. 
if there remains no unbound c(j)'s (j < i or j > 
i) the sentence is parsed. If c(i+l) exists go to 
the step 1 with i = i+1. if c(i+l) doesn't exist 
or the steps followed previous case returned a 
failure, go to the step 4 with c(i) • pop 
(PENDING). 
5b) The environment description h•• not been 
matched. Return a failure. 
2b 4 
........... 
Figure 4. The transition network model of the 
control strategy. 
222 
3.2 The implementation of the control atrategy 
The control system has two levels: the basic level 
employs a generat two-way automaton and the upper 
level uses a blackboard system. There is a ctear 
correspondence between the grammar description and the 
control system: the two-way automaton makes local 
decisions according to the binary relations. These 
local decisions are controlled by the blackboard 
system which utilizes the environment descriptions 
written in the schemata. This two-level control model 
has certain advantages. The two-way automaton is 
computationalty efficient in local decisions. On the 
other hand, the blackboard system is able to utilize 
global knowledge of the input sentence. 
ChronoLogicat backtracking 
To account for ambiguities there are three kinds of 
backtracking points in the control system. 
Backtracking may be done in regard to choice of 
dependency functions, homographic word forms, or 
associated schemata. Backtracking is chronological. 
In our system a constituent c(|) may contain several 
different morphotactic interpretations of a word form. 
Function backtracking takes place if there are several 
possible binary relations between a given constituent 
pair. The preconditions of the schemata may allow 
multiple schema candidates for a given constituent. 
All alternatives are gone through one by one, if 
necessary, in chronological backtracking. As a 
result, the system may perform an exhaustive search 
and produce all possible solutions. 
Register for tong-distance dependencies 
The recognition of possible fond-distant dependencies 
is done by the capture function. An element is bound 
as a possible "distant member" in the context where 
the capture function fires. An element is also moved 
to the special register for s set of distant elements. 
The actual binding is done by the distant function 
from another schema. In chronological backtracking 
also distant bindings are undone. 
The strategy of local decisions controlled by global 
knowledge of the input sentence yields a strongly 
data-driven, taft-to-right and bottom-up parse whereby 
partial dependency trees are built proceeding from 
middle to out. 
3.3 EZANPLES 
To v|suatize our discussion, a functional schema 
IntrllapNegVP is described in Figure 5. A grammar 
writer has declared in WHEN-part that R must be a 
transitive process verb in active tense snd Imperative 
mood. In its taft context there must be a negative 
verb in imperative mood and of the textcat form "El" 
("NOT"). There is one obligatory dependency retstion 
HegVerb. Adverbials may exist multiple times. A 
grammar writer has written in clauses LEFT and RIGHT 
the left and right context binary relations of the 
regent. After the schema has fully matched, the 
regent is marked VerbP and features PersonH and 
PersonP of the dependent recognized as HegVerb are 
marked for the regent. 
($CHEHA: lntrlmperNegVP 
WHEN (AND 
(R : ProcVerb Act Imper (NOT VerbTr)) 
(Left = 'E% Imper)) 
FUNCTIONS (OBLIGATORY NegVerb) 
(NULTIPLE AdverbiaL) 
(LEFT NegVerb Adverbial Connect) 
(RIGHT AdverbiaL) 
)lARK (R :- VerbP (RecNegVerb (PersonP PersonH))) 
) 
Figure S. A functional schema lntrlmperMegVP 
A futt trace of parsing the sentence "~ti eksy 
mets~ss~l" (Don't get lost in s forest) appears in 
Figure 6. Parsing starts from the taft Can arrow). 
Next tins indicates the selected schema and dependents 
that are tested. The first word "itS" is identified 
ms a negative imperative verb with no dependents 
(schema DummyVP ok). The imperative verb "eksy" (to 
get lost) is then tried by the schema 
IntrlmperNegVP. The binary relation NegVerb holds 
between the two verbs, and the corresponding 
dependency function adjoins them. The othen functions 
fail. Dependents are searched next from the right 
context. The control proceeds to the word "mets~ss~" 
(forest). For that word no dependents are found and 
the system returns to the unfinished regent "eksy". 
The schema IntrlmperNegVP has onty two relations 
remaining: Connect and Adverb|at. The word 
"nets~ss~" is bound as an adverbial. The schema has 
been fully matched and the Input sentence is 
completely parsed. 
223 
> it~ eksy metsissil 
NORFO: 
(((("iti" EI Verb Act Imper Pr S /2P/))) 
((("eksy" EKSY~ Verb Act Imper Pr S /2P/))) 
((("metsissi" METSX Noun SG In))) 
((("!" EXCLAMATION)))) 
:> (iti) (eksy) (metsissi) 
Schema: OummyVP nit 
OummyVP ok 
(iti) :> (eksy) (metsissi) 
Schema: lntrlmperNegVP (Negverb Adverbial Connect) 
NegVerb ok 
Adverbial failed 
Connect failed 
((iti) eksy) => (metsissi) 
Schema: TriviatSP (DefPart R) 
DefPart failed 
TrivialSP ok 
returning to unfinished constituent... 
((ili) eksy) <= (metsissi) 
Schema: IntrlmperNegVP (Connect Adverbial) 
Adverbial ok 
IntrlmperNegVP ok 
=> ((iti) eksy (metstssi)) PARSED 
The parse took 0.87 seconds CPU-time on VAX-11/751. 
Figure 6. An example of parsing. 
The second example shows how our parser solves the 
following sentence (adopted from Karttunen, 1986b) 
which has a tong-distance dependency: 
En mini tennisti a|o ruveta petaamaan. 
not I tennis intend start play 
I do not intend to start to play tennis. 
The object of the subordinated infinitiviat clause 
("tennisti") has been raised in the main clause thus 
creating a gap. The parse tree of the sentence is in 
Figure 7. 
aid 
Predicate 
I 
......... • ......... • ......... ÷ ......... • 
I I I I 
en mini tenni$tl ruvetl 
Negation Subject Distant(I) Object 
i 
i 
peteamaen 
Adverbial 
I . ......... + 
I 
tennilti 
Object(I) 
Figure 7. An example of a tong-distance dependency. 
In the parsing process the schema NO-VP has matched 
the environment of the verb "a|o" (intend) and the 
schema O-LocativeVP of the verb "peLaamaan" (play). 
(SCHEMA: NO-VP 
ASSUME (R :, Negative) 
FUNCTIONS (OBLIGATORY Object Negation) 
(KULTIPLE Adverb|at OistentMember) 
(LEFT Auxiliary Negation Object Adverbial Connector) 
(RIGHT Object Adverbial Cor~'na) 
(CAPTURE OistantNember) 
CLAUSE READY 
CHEC~ (VerbObjCongr Negation Object) 
MARK (R := ProcVP Predicate (Negation (PersonP PersonN))) 
) 
(SCHEHA: 
FUNCTIONS 
MARK 
) 
O-LocativeVP 
(OBLIGATORY Object) 
(HULTIPLE Adverbial OistentNember) 
(RIGHT Object Adverbial) 
(LEFT Object Adverbiat) 
(CAPTURE P|stantRember) 
(DISTANT Object Adverb|it) 
(R :s LocetlveVP Pred|cete) 
The schema NO-VP has captured the word "tennisti" as a 
DistantNember. The schema O-LocattveVP has Later on 
bound it as a removed Object. 
4 COliPARISON 
The notion of unification has recently emerged as a 
common descriptive device in many Linguistic theories 
Like FUG, PATR-\[\[ and HPSG (Shieber 1986). Another 
popular approach has been to apply attribute grammars 
originally developed as a theory for formal Languages 
(gnuth 1968). LFG and OCG can be viewed as attribute 
grammar systems. The trend has been towards strictly 
declarative descriptions of syntactic structure. 
Syntactic rules are often expressed in the form of 
complex feature sets. 
Our ADP system also uses features, but differs both 
from the unification-based approach and attribute 
grammar approach. The basic difference is, of course, 
that there is neither unification nor correspondence 
to attribute grammars in our system. We use a pattern 
matching via binary relation tests. Through 
blackboard approach we have gained a flexible control. 
Blackboard system can conveniently take into account 
global knowledge of the sentence. In our model 
dependents become "hidden" from further processing 
once they have been found. A regent solely represents 
the constituents hanging below. This makes the 
parsing process simpler as the number of constituents 
decreases during parsing. There ere, however, some 
cases where some information must be raised from the 
dependent to the regent (e.g. from conjuncts to the 
conjunction), so that the regent could represent the 
whole constituent. 
224 
5 CONCLUSION 
In our system linguistic knowledge and processing 
mechanisms are separated. Structural information of 
the functional schemata is interpreted by the 
blackboard scheduler as control knowledge, according 
to which dependencies are searched. The difference 
between local and global decisions is clearly 
separated. Locat decisions controlled by global 
knowledge of the input sentence has made it possible 
to find solutions for problems that are difficult to 
solve in traditional parsing systems. ADP finds all 
solutions for an ambiguous sentence. Augmented search 
process covers tong-distance dependencies as well. 
Different criteria have been expressed for grammar 
formalisms (Winogrsd 1983, Karttunen 1986a): 
perspicuity, nondirectionstity, correspondence with 
meanings, multiple dimensions of patterning, 
order-independency, declarativeness and monotontc~ty. 
Our model rates welt in most of these criteria. 
Perspicuity, correspondence with meanings and 
dectarsttveness are satisfied in the way the 
functional schemata describe local environments for 
regents. The functional description is monotonic and 
allows multiple dimensions of patterning. 
There is s process of parsing as s starting point in 
the grammar specification, so it lacks 
nondirectionatity. The weakest point is the 
order-dependent control mechanism, albeit the grammar 
description is order-lndependent. Plans for the 
general, order-independent control strategy have been 
done. 
ADP has been implemented in FranzLisp. Experiments 
w~tn a non-trivial set of Finnish sentence structures 
has been performed on VAX 11/751 system. An average 
time for parsing a six word sentence is between 0.5 
and 2.0 seconds for the first parse. At the moment 
the grammar description contains common sentence 
structures quite well. There are 66 binary relations, 
188 functional schemata and 1800 lexicon entries. The 
lexicon of the morphological analyzer contains 35 000 
words. 
ACKNOWLEDGENENT$ 
This research has been supported by SlTRA Foundation. 
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