LICENSING AND TREE ADJOINING GRAMMAR IN 
GOVERNMENT BINDING PARSING 
Robert Frank* 
Department of Computer and Information Sciences 
University of Pennsylvania 
Philadelphia, PA 19104 
email: frank@ linc.cis.upenn.edu 
Abstract 
This paper presents an implemented, psychologically plau- 
sible parsing model for Government Binding theory gram- 
mars. I make use of two main ideas: (1) a generaliza- 
tion of the licensing relations of \[Abney, 1986\] allows for 
the direct encoding of certain principles of grammar (e.g. 
Theta Criterion, Case Filter) which drive structure build- 
ing; (2) the working space of the parser is constrained 
to the domain determined by a Tree Adjoining Grammar 
elementary tree. All dependencies and constraints are lo- 
caiized within this bounded structure. The resultant parser 
operates in linear time and allows for incremental semantic 
interpretation and determination of grammaticaiity. 
1 Introduction 
This paper aims to provide a psychologically plausible 
mechanism for putting the knowledge which a speaker 
has of the syntax of a language, the competence gram- 
mar, to use. The representation of knowledge of language 
I assume is that specified by Government Binding (GB) 
Theory introduced in \[Chomsky, 1981\]. GB, as a com- 
petence theory, emphatically does not specify the nature 
of the language processing mechanism. In fact, "proofs" 
that transformational grammar is inadequate as a linguis- 
tic theory due to various performance measures are funda- 
mentally flawed since they suppose a particular connection 
between the grammar and parser \[Berwick and Weinberg, 
1984\]. Nonetheless, it seems desirable to maintain a fairly 
direct connection between the linguistic competence and 
*I would like to thank the following for their valuable discussion and 
suggestions: Naoki Fukui, Jarnie Henderson, Aravind Joshi, Tony Kroch, 
Mitch Marcus, Michael Niv, Yves Schabes, Mark Steedman, Enric Vall- 
duv{. This work was pa~ially supported by ARO Grants DAAL03-89- 
C0031 PRI and DAAG29-84-K-0061 and DARPA grant N00014-85-K- 
0018. The author is supported by a Unisys doctoral fellowship. 
its processing. Otherwise, claims of the psychological re- 
ality of this particular conception of competence become 
essentially vacuous since they cannot be falsified but for 
the data on which they are founded, i.e. grammaticality 
judgments. Thus, in building a model of language pro- 
cessing, I would like to posit as direct a link as is possible 
between linguistic competence and the operations of the 
parser while still maintaining certain desirable computa- 
tional properties. 
What are the computational properties necessary for 
psychological plausibility? Since human syntactic pro- 
cessing is an effortless process, we should expect that it 
take place efficiently, perhaps in linear time since sen- 
tences do not become more difficult to process simply 
as a function of their length. Determinism, as proposed 
by Marcus \[1980\], seems desirable as well. In addition, 
the mechanism should operate in an incremental fashion. 
Incrementality is evidenced in the human language pro- 
cessor in two ways. As we hear a sentence, we build 
up semantic representations without waiting until the sen- 
tence is complete. Thus, the semantic processor should 
have access to syntactic representations prior to an utter- 
ance's completion. Additionally, we are able to perceive 
ungrammaticality in sentences almost immediately after 
the ill fonnedness occurs. Thus, our processing mecha- 
nism should mimic this early detection of ungrammatical 
input. 
Unfortunately, a parser with the most transparent rela- 
tionship to the grammar, a "parsing as theorem proving" 
approach as proposed by \[Johnson, 1988\] and \[Stabler, 
1990\], does not fare well with respect to our computa- 
tional desiderata. It suffers from the legacy of the com- 
putational properties of first order theorem proving, most 
notably undecidability, and is thus inadequate for our pur- 
poses. The question, then, is how much we must repeat 
from this direct instantiatiou so that we can maintain the 
requisite properties. In this paper, I attempt to provide 
iii 
an answer. I propose a parsing model which represents 
the principles of the grammar in a fairly direct manner, 
yet preserves efficiency and incrementality. The model 
depends upon two key ideas. First, I utilize the insight 
of \[Abney, 1986\] in the use of licensing relations as the 
foundation for GB parsing. By generalizing Abney's for- 
mulation of licensing, I can directly encode and enforce 
a particular class of the principles of GB theory and in 
so doing efficiently build phrase structure. The principles 
expressible through licensing are not all of those posited 
by GB. Thus, the others must be enforced using a different 
mechanism. Unfortunately, the unbounded size of the tree 
created with licensing makes any such mechanism compu- 
tationally abhorrent. In order to remedy this, I make use 
of the Tree Adjoining Grammar (TAG) framework \[Joshi, 
1985\] to limit the working space of the parser. As the 
parser proceeds, its working slructure is bounded in size. 
If this bound is exceeded, we reduce this structure by one 
of the operations provided by the TAG formalism, either 
substitution or adjunction. This results in two structures, 
each of which form independent elementary trees. Inter- 
estingly, the domain of locality imposed by a TAG ele- 
mentary tree appears to be sufficient for the expression of 
the remaining grammatical principles. Thus, we can check 
for the satisfaction of the remaining grammatical princi- 
ples in just the excised piece of structure and then send it 
off for semantic interpretation. Since this domain of con- 
straint checking is bounded in size, this process is done 
efficiently. This mechanism also works in an incremental 
fashion. 
2 Abney's Licensing 
Since many grammatical constraints are concerned with 
the licensing of elements, Abney \[1986\] proposes utiliz- 
ing licensing structure as a more concrete representation 
for parsing. This allows for more efficient processing yet 
maintains "the spirit of the abstract grammar." 
Abney's notion of licensing requires that every element 
in a structure be licensed by performing some syntac- 
tic function. Any structure with unlicensed elements is 
ill-formed. Abney takes them role assignment to be the 
canonical case of licensing and assumes that the properties 
of a general licensing relation should mirror those of theta 
assignment, namely, that it be unique, local and lexical. 
The uniqueness proporty for them assignment requires that 
an argument receives one and only one them role. Corre- 
spondingly, licensing is unique: an element is licensed via 
exactly one licensing relation. Locality demands that theta 
assignment, and correspondingly licensing, take place un- 
der a strict definition of government: sisterhood. Finally, 
112 
IP 
NP will vpSM~ 
M ry tomorrow 
~T...~ 
Figure 1: Abney's Licensing Relations in Clausal Struc- 
ture (S = subjecthood, F = functional selection, M = mod- 
ification, T = theta) 
theta assignment is lexical in that it is the properties of the 
the theta assigner which determine what theta assignment 
relations obtain. Licensing will have the same property; it 
is the licenser that determines how many and what sort of 
elements it licenses. 
Each licensing relation is a 3-tuple (D, Cat, Type). D is 
the direction in which licensing occurs. Cat is the syntac- 
tic category of the element licensed by this relation. Type 
specifies the linguistic function accomplished by this li- 
censing relation. This can be either functional selection, 
subjecthood, modification or theta-assignment. Functional 
selection is the relation which obtains between a func- 
tional head and the element for which it subcategorizes, 
i.e. between C and IP, I and VP, D and NP. Subjecthood 
is the relation between a head and its "subject". Moditica- 
tion holds between a head and adjunct. Theta assignment 
occurs between a head and its subeategnrized elements. 
Figure 1 gives an example of the licensing relations which 
might obtain in a simple clause. Parsing with these li- 
censing relations simply consists of determining, for each 
lexieal item as it is read in a single left to right pass, 
where it is licensed in the previously constructed structure 
or whether it licenses the previous structure. 
We can now re-examine Abney's claim that these licens- 
ing relations allow him to retain "the spirit of the abstract 
grammar." Since licensing relations talk only of very lo- 
cal relationships, that occurring between sisters, this sys- 
tem cannot enforce the constraints of binding, control, and 
ECP among others. Abney notes this and suggests that his 
licensing should be seen as a single module in a parsing 
system. One would hope, though, that principles which 
have their roots in licensing, such as those of theta and 
case theory, could receive natural treatments. Unfortu- 
nately, this is not true. Consider the theta criterion. While 
this licensing system is able to encode the portion of the 
constraint that requires theta roles to be assigned uniquely, 
it fails to guarantee that all NPs (arguments) receive a theta 
role. This is crucially not the case since NPs are some- 
times licensed not by them but by subject licensing. Thus, 
the following pair will be indistinguishable: 
i. It seems that the pigeon is dead 
ii. * Joe seems that the pigeon is dead 
Both It and Joe will be appropriately licensed by a subject 
licensing relation associated with seems. The case filter 
also cannot be expressed since objects of ECM verbs are 
"licensed" by the lower clause as subject, yet also require 
case. Thus, the following distinction cannot accounted for: 
i. Carol asked Ben to swat the fly 
ii. * Carol tried Ben to swat the fly 
Here, in order to get the desired syntactic structure (with 
Ben in the lower clause in both cases), Ben will need to 
be licensed by the inflectional element to. Since such a 
licensing relation need be unique, the case assigning prop- 
erties of the matrix verbs will be irrelevant. What seems 
to have happened is that we have lost the modularity of the 
the syntactic relations constrained by grammatical princi- 
ples. Everything has been conltated onto one homoge- 
neous licensing structure. 
3 Generalized Licensing 
In order to remedy these deficiencies, I propose a system 
of Generalized Licensing. In this system, every node is 
assigned two sets of licensing relations: gives and needs. 
Gives are similar to the Abney's licensing relations: they 
are satisfied locally and determined lexically. Needs spec- 
ify the ways in which a node must be licensed.1 A need 
of type them, for example, requires a node to be licensed 
by a theta relation. In the current formulation, needs differ 
from gives in that they are always directionaUy unspeci- 
fied. We can now represent the theta criterion by placing 
theta gives on a theta assigner for each argument and theta 
needs on all DPs. This encodes both that all them roles 
must be assigned and that all arguments must receive theta 
roles. 
In Generalized Licensing, we allow a greater vocabu- 
lary of relation types: case, them assignment, modification, 
functional selection, predication, f-features, etc. We can 
then explicitly represent many of the relations which are 
posited in the grammar and preserve the modularity of the 
theory. As a result, however, certain elements can and 
must be multiply licensed. DPs, for instance, will have 
needs for both them and case as a result of the case filter 
and theta criterion. We therefore relax the requirement that 
1These bear some similarity to the anti-relations of Abney, but are 
used in a rather different fashion. 
113 
all nodes be uniquely licensed. Rather, we demand that 
all gives and needs be uniquely "satisfied." The unique- 
ness requirement in Abney's relations is now pushed down 
to the level of individual gives and needs. Once a give 
or need is satisfied, it may not participate in any other 
licensing relationships. 
One further generalization which I make concerns the 
positioning of gives and needs. In Abney's system, licens- 
ing relations are associated with lexical heads and applied 
to maximal projections of other heads. Phrase structure is 
thus entirely parasitic upon the reconstruction of licensing 
structure. I propose to have an independent process of 
lexical projection. A lexical item projects to the correct 
number of levels in its maximal projection, as determined 
by theta structure, f-features, and other lexical properties. 2 
Gives and needs are assigned to each of these nodes. As 
with Abney's system, licensing takes place under a strict 
notion of government (sisterhood). However, the projec- 
tion process allows licensing relations determined by a 
head to take place over a somewhat larger domain than 
sisterhood to the head. A DP's theta need resulting from 
the them criterion, for example, is present only at the max- 
imal projection level. This is the node which stands in the 
appropriate structural relation to a theta give. As a re- 
sult of this projection process, though, we must explicitly 
represent structural relations during parsing. 
The reader may have noticed that multiple needs on a 
node might not all be satisfiable in one structural position. 
Consider the case of a DP subject which possesses both 
theta and case needs. The S-structure subject of the sen- 
tence receives its theta role from the verb, yet it receives its 
case from the tense/agreement morpheme heading IP. This 
is impossible, though, since given the structural correlate 
of the licensing relation, the DP would then be directly 
dominated both by IP and by VP. Yet, it cannot be in ei- 
ther one of these positions alone, since we will then have 
unsatisfied needs and hence an ill-formed structure. Thus, 
our representation of grammatical principles and the con- 
straints on give and need satisfaction force us into adopt- 
ing a general notion of chain and more specifically the VP 
internal subject hypothesis. A chain consist of a list of 
nodes (al .... ,a~) such that they share gives and needs and 
each ai c-commands each a~+l. The first element in the 
chain, al, the head, is the only element which can have 
phonological content. Others must be empty categories. 
Now, since the elements of the chain can occupy differ- 
ent structural positions, they may be governed and hence 
licensed by distinct elements. In the simple sentence: 
\[IP Johns tns/agr \[V' ti smile\]\] 
21 assume the relativized X-bar theory proposed in \[Fukui and Speas, 
1986\]. 
the trace node which is an argument of smile forms a chain 
with the DP John. In its V' internal position, the theta 
need is satisfied by the theta give associated with the V. 
In subject position, the case need is satisfied by the case 
give on the I' projection of the inflectional morphology. 
Now, how might we parse using these licensing rela- 
tions? Abney's method is not sufficient since a single 
instance of licensing no longer guarantees that all of a 
node's licensing constraints are satisfied. I propose a sim- 
ple mechanism, which generalizes Abney's approach: We 
proceed left to right, project the current input token to its 
maximal projection p and add the associated gives and 
needs to each of the nodes. These are determined by ex- 
amination of information in the lexical entries (such as 
using the theta grid to determine theta gives), examination 
of language specific parameters (using head directionality 
in order to determined directionality of gives, for exam- 
pie), and consultation of UG parameters (for instance as a 
result of the case filter, every DP maximal projection will 
have an associated case need). The parser then attempts to 
combine this projection with previously built structure in 
one of two ways. We may attach p as the sister of a node 
n on the right frontier of the developing structure, when 
p is licensed by n either by a give in n and/or a need in 
the node p. Another possibility is that the previously built 
structure is attached as sister to a node, rn, dominated by 
the maximal projection p, by satisfying a give in rn and/or 
a need on the root of the previously built structure. In the 
case of multiple attachment possibilities, we order them 
according to some metric such as the one proposed by 
Abney, and choose the most highly ranked option. 
As structure is built, nodes in the tree with unsatisfied 
gives and needs may become closed off from the right 
frontier of the working structure. In such positions, they 
will never become satisfied. In the ease of a need in an 
internal node n which is unsatisfied, we posit the existence 
of an empty category rn, which will be attached later to 
the structure such that (n, ra) form a chain. We posit an 
element to have been moved into a position exactly when it 
is licensed at that position yet its needs are not completely 
satisfied. After positing the empty category, we push it 
onto the trace stack. When a node has an unsatisfied give 
and no longer has access to the right frontier, we must posit 
some element, not phonologically represented in the input, 
which satisfies that give relation. If there is an element on 
the top of the trace stack which can satisfy this give, we 
pop it off the stack and attach it. 3 Of course, if the trace 
has any remaining needs, it is returned to the Pace stack 
since its new position is isolated from the right frontier. 
If no such element appears on top of the mace stack, we 
3Note that the use of this stack to recover filler-gap structures forbids 
non-nested dependencies as in \[Fodor, 1978\]. 
IP 
/~ 8tree: <left, case, nomlaattve, 1> i 
needs: <th~, ?, ?> needs: 
<caae, non~ative, ~>Harry ! styes: <rlsht, ~anctioc.-select, VP, ?> 
Figure 2: Working Space after "Harry tns/agr" 
posit a non-mace empty category of the appropriate type, 
if one exists in the language. 4 
Let's try this mechanism on the sentence "Harry 
laughs." The first token received is Harry and is projected 
to DP. No gives are associated with this node, but them 
and case needs are inserted into the need set as a result 
of the them criterion and the case filter. Next, tns/agr is 
read and projected to I", since it possesses f-features (cf. 
\[Fuktti and Speas, 1986\]). Associated with the I ° node 
is a rightward functional selection give of value V. On 
the I' node is a leftward nominative case give, from the 
f-features, and a leftward subject give, as a result of the 
Extended Projection Principle. The previously constructed 
DP is attached as sister to the I' node, thereby satisfying 
the subject and case gives of the I' as well as the case need 
of the DP. We are thus left with the structure in figure 2. 5 
Next, we see that the them need of the DP is inaccessible 
from the right frontier, so we push an empty category DP 
whose need set contains this unsatisfied theta need onto 
the mace stack. The next input token is the verb laugh. 
This is projected to a single bar level. Since laugh assigns 
an external theta role, we insert a leftward theta give to 
a DP into the V' node. This verbal projection is attached 
as sister to I °, satisfying the functional selection give of 
I. However, the theta give in V' remains unsatisfied and 
since it is leftward, is inaccessible. We therefore need to 
posit an empty category. Since the DP trace on top of the 
trace stack will accept this give, the trace stack is popped 
and the trace is attached via Chomsky-adjunction to the 
4Such a simplistic approach to determining whether a trace or non- 
trace empty category should be inserted is dearly not correct. For in- 
stance, in "tough movement" 
Alvin i is tough PRO to feed ti 
the proposed mechanism will insert the trace of Alvin in subject posi- 
tion rather than PRO. It remains for future work to determine the exact 
mechanism by which such decisions are made. 
5In the examples which follow, gives are shown as 4-topics 
(D,T~tpe,Val, SatBI/) where D is the direction, T~tpe is the type 
of licensing relation, Val is the licensing relation value and SatB~ is 
the node which satisfies the give (marked as 7, if the relation is as yet 
unsatisfied). Needs are 3-tuples (Type, Val, SatB~/) where these are 
as in the gives. For purposes of readability, I remove previously satisfied 
gives and needs from the fgure. Of course, such information persists in 
the parser's representation. 
114 
IP 
81ve~ o 
Harry 
need= ~ 
*~.eds: ,eh~,a, aS~, *,> 
V 
I laush Figure 4: Adjunction of auxiliary tree/~ into elementary tree ~ to produce 7 
Figure 3: Working space after "Harry tns/agr laugh" 
V' node yielding the structure in figure 3. Since this node 
forms a chain with the subject DP, the theta need on the 
subject DP is now satisfied. We have now reached the end 
of our input. The resulting structure is easily seen to be 
well-formed since all gives and needs are satisfied. 
We have adopted a very particular view of traces: their 
positions in the structure must be independently motivated 
by some other licensing relation. Note, then, that we can- 
not analyze long distance dependencies through successive 
cyclic movement. There is no licensing relation which will 
cause the intermediate traces to exist. Ordinarily these 
traces exist only to allow a well-formed derivation, i.e. 
not ruled out by subjacency or by a barrier to antecedent 
government. Thus, we need to account for constraints on 
long distance movement in another manner. We will return 
to this in the next section. 
The mechanism I have proposed allows a fairly direct 
encoding for some of the principles of grammar such as 
case theory, them theory, and the extended projection prin- 
ciple. However, many other constraints of GB, such as the 
ECP, control theory, binding theory, and bounding the- 
try, cannot be expressed perspicuously through licensing. 
Since we want our parser to maintain a fairly direct con- 
nection with the grammar, we need some additional mech- 
anism to ensure the satisfaction of these constraints. 
Recall, again, the computational properties we wanted to 
hold of our parsing model: efficiency and incrementality. 
The structure building process I have described has worst 
case complexity O(n 2) since the set of possible attach- 
ments can grow linearly with the input. While not enor- 
mously computationally intensive, this is greater that the 
linear time bound we desire. Also, checking for satisfac- 
tion of non-licensing constraints over unboundedly large 
structures is likely to be quite inefficient. There is also 
the question of when these other constraints are checked. 
To accord with incrementality, they must be checked as 
soon as possible, and not function as post-processing "fil- 
ters." Unfortunately, it is not easily determinable when a 
given constraint can apply such that further input will not 
change the status of the satisfaction of a constraint. We 
do not want to rule a structure ungrammatical simply be- 
cause it is incomplete. Finally, it is unclear how we might 
incorporate this mechanism which builds an ever larger 
syntactic structure into a model which performs semantic 
interpretation incrementally. 
4 Limiting the Domain with TAG 
These problems with our model are solved if we can place 
a limit on the size of the structures we construct. The 
number of licensing possibilities would be bounded yield- 
ing linear time for smacture construction. Also, constraint 
checking could be done in a constant amount of process- 
ing. Unfortunately, the productivity of language requires 
us to handle sentences of unbounded length and thus lin- 
guistic structures of unbounded size. 
TAG provides us with a way to achieve this paradise. 
TAG accomplishes linguistic description by factoring re- 
cursion from local dependencies \[Joshi, 1985\]. It posits 
a set of primitive structures, the elementary trees, which 
may be combined through the operations of adjunction 
and substitution. An elementary tree is a minimal non- 
recursive syntactic tree, a predication structure containing 
positions for all arguments. I propose that this is the pro- 
jection of a lexical head together with any of the associated 
functional projections of which it is a complement. For 
instance, a single elementary tree may contain the projec- 
tion of a V along with the I and C projections in which it 
is embedded. 6 Along the frontier of these trees may ap- 
pear terminal symbols (i.e. lexical items) or non-terminals. 
The substitution operation is the insertion of one elemen- 
tary tree at a non-terminal of same type as the root on the 
frontier of another elementary tree. Adjunction allows the 
insertion of one elementary tree of a special kind, an aux- 
iliary tree, at a node internal to another (cf. figure 4). In 
6This definition of TAG elementary trees is consistent with the Lex- 
icalized TAG framework \[Schabes et al., 1988\] in that the lexical head 
may be seen as the anchor of the elementary trees. For further details 
and consequences of this proposal on elementary tree weU-fomaedness, 
see \[Frank, 1990\]. 
115 
auxiliary trees, there is a single distinguished non-terminal 
on the frontier of the tree, the foot node, which is iden- 
tical in type to the root node. Only adjunctions, and not 
substitutions, may occur at this node. 
TAG has proven useful as a formalism in which one can 
express linguistic generalizations since it seems to provide 
a sufficient domain over which grammatical constraints 
can be stated \[Kroch and Joshi, 1985\] \[Kroch and San- 
torini, 1987\]. Kroch, in two remarkable papers \[1986\] and 
\[1987\], has shown that even constraints on long distance 
dependencies, which intuitively demand a more "global" 
perspective, can be expressed using an entirely local (i.e. 
within a single elementary lee) formulation of the ECP 
and allows for the collapsing of the CED with the ECP. 
This analysis does not utilize intermediate traces, but in- 
stead the link between filler and gap is "stretched" upon 
the insertion of intervening structure during adjunctions. 
Thus, we are relieved of the problem that intermediate 
traces are not licensed, since we do not require their exis- 
tence. 
Let us suppose a formulation of GB in which all princi- 
ples not enforced through generalized licensing are stated 
over the local domain of a TAG elementary tree. Now, 
we can use the model described above to create structures 
corresponding to single elementary trees. However, we 
restrict the working space of the parser to contain only a 
single structure of this size. If we perform an attachment 
which violates this "memory limitation," we are forced to 
reduce the structure in our working space. We will do 
this in one of two ways, corresponding to the two mech- 
anisms which TAG provides for combining structure. Ei- 
ther we will undo a substitution or undo an adjunction. 
However, all chains are required to be localized in indi- 
vidual elementary tree. Once an elementary tree is fin- 
ished, non-licensing constraints are checked and it is sent 
off for semantic interpretation. This is the basis for my 
proposed parsing model. For details of the algorithm, see 
\[Frank, 1990\]. This mechanism operates in linear time and 
deterministically, while maintaining coarse grained (i.e. 
clausal) incrementality for grammaticality determination 
and semantic interpretation. 
Consider this model on the raising sentence "Harry 
seemed to kiss Sally." We begin as before with "Harry 
tns/agr" yielding the structure in figure 2. Before we re- 
ceive the next token of input, however, we see that the 
working structure is larger than the domain of an elemen- 
tary tree, since the subject DP constitutes an independent 
predication from the one determined by the projection of 
I. We therefore unsubstitute the subject DP and send it off 
to constraint checking and semantic interpretation. At this 
point, we push a copy of the subject DP node onto the 
trace stack due to its unsatisfied theta need. 
116 
IP 
~'~ ~ t~i I' .,.~ <am.. r. r> /" J~ 
6iw~ <risht, funclima-sdea. VP, k> I 
ux./aSr SiVm~ ~ ~ 1P. r> 
Figure 5: Working space after "Harry tus/agr seem" 
IP 
n~di: <lhela, ?, ?> 
x I' 
sirra: <rl~t, there, u', t> v ~' n~it 
n~d.:e .~l ~sivm: P ~ Jm~,t'nma-m~ vP. r> 
Figure 6: Working space after "Harry tns/agr seem to" 
We continue with the verb seem which projects to V' 
and attaches as sister to I satisfying the functional selec- 
tion give yielding the structure in figure 5. There remains 
only one elementary tree in working space so we need 
not perform any domain reduction. Next, to projects to I' 
since it lacks f-features to assign to its specifier. This is 
attached as object of seem as in figure 6. At this point, 
we must again perform a domain reduction operation since 
the upper and lower clauses form two separate elementary 
trees. Since the subject DP remains on the trace stack, it 
cannot yet be removed. All dependencies must be resolved 
withina single elementary tree. Hence, we must unadjoin 
the structure recursive on I' shown in figure 7 leaving the 
structure in figure 8 in the working space. This structure 
is sent off for constraint checking and semantic interpreta- 
tion. We continue with kiss, projecting and attaching it as 
functionally selected sister of I and popping the DP from 
the trace stack to serve as external argument. Finally, we 
I' /N 
I V' 
tns/agr V I' 
I 
Figure 7: Result of unadjunction 
IP 
/~, Stves: <le*%, subject, DP, i> &,iv~ e DPii \]I need.: ~ 
needs: <theta, ?, ?> / <l'~ht, funct ton-select, 
i 
to 
VP, ?> 
Figure 8: Working space after unadjunction 
constrained, we might be able to retain the efficient nature 
of the current model. Other strategies for resolving such 
indeterminacies using statistical reasoning or hard coded 
rules or templates might also be possible, but these con- 
structs are not the sort of grammatical knowledge we have 
been considering here and would entail further abstraction 
from the competence grammar. 
Another problem with the parser has to do with the 
incompleteness of the algorithm. Sentences such as 
IP 
v, 
V DP 
I kiss 
Figure 9: Working Structure after entire sentence 
project and attach the DP Sally as sister of V, receiving 
both them role and case in this position. This DP is unsub- 
stituted in the same manner as the subject and is sent off 
for further processing. We are left finally with the struc- 
ture in figure 9, all of whose gives and needs are satisfied, 
and we are finished. 
This model also handles control constructions, bare in- 
finitives, ECM verbs and binding of anaphors, modifica- 
tion, genitive DPs and others. Due to space constraints, 
these are not discussed here, but see \[Frank, 1990\]. 
5 Problems and Future Work 
Boris knew that Tom ate lunch 
will not be parsed even though there exist well-formed 
sets of elementary trees which can derive them. The prob- 
lem results from the fact that the left to right processing 
strategy we have adopted is a bit too strict. The comple- 
mentizer that will be attached as object of know, but Tom 
is not then licensed by any node on the right frontier. Ul- 
timately, this DP is licensed by the tns/agr morpheme in 
the lower clause whose IP projection is licensed through 
functional selection by C. Similarly, the parser would have 
great difficulty handling head final languages. Again, these 
problems might be solved using extra-grammatical de- 
vices, such as the attention shifting of \[Marcus, 1980\] or 
some template matching mechanism, but this would entail 
a process of "compiling out" of the grammar that we have 
been trying to avoid. 
Finally, phonologically empty heads and head move- 
ment cause great difficulties for this mechanism. Heads 
play a crucial role in this "project and attach" scheme. 
Therefore, we must find a way of determining when and 
where heads occur when they are either dislocated or not 
present in the input string at all, perhaps in a similar man- 
ner to the mechanism for movement of maximal projec- 
tions I have proposed above. 
The parsing model which I have presented here is still 
rather preliminary. There are a number of areas which will 
require further development before this can be considered 
complete. 
I have assumed that the process of projection is en- 
tirely determined from lexieal lookup. It is clear, though, 
that lexical ambiguity abounds and that the assignment of 
gives and needs to the projections of input tokens is not 
determinate. An example of such indeterminacy has to do 
with the assignment to argument maximal projections of 
theta needs as a result of the them criterion. DPs need not 
always function as arguments, as I have been assuming. 
This problem might be solved by allowing for the state- 
ment of disjunctive constraints or a limited form of paral- 
lelism. If the duration of such parallelism could be tightly 
6 Conclusion 
In this paper, I have sketched a psychologically plausible 
model for the use of GB grammars. The currently im- 
plemented parser is a bit too simple to be truly robust, 
but the general approach presented here seems promising. 
Particularly interesting is that the computationally moti- 
vated use of TAG to constrain processing locality pro- 
vides us with insight on the nature of the meta-grammar 
of possible grammatical constraints. Thus, if grammatical 
principles are stated over such a bounded domain, we can 
guarantee the existence of a perspicuous model for their 
use, thereby lending credence to the cognitive reality of 
this competence grammar. 
117 
References 
\[Abney, 1986\] Steven Abney. Licensing and parsing. In 
Proceedings of NELS 16, Amherst, MA. 
\[Berwick and Weinberg, 1984\] Robert Berwick and Amy 
Weinberg. The Grammatical Basis of Linguistic Per- 
formance. MIT Press, Cambridge, MA. 
\[Chomsky, 1981\] Noam Chomsky. Lectures on Govern- 
ment and Binding. Foris, Dordrecht. 
\[Fodor, 1978\] Janet D Fodor. Parsing strategies and con- 
straints on transformations. Linguistic Inquiry, 9. 
\[Frank, 1990\] Robert Frank. Computation and Linguistic 
Theory: A Government Binding Theory Parser Using 
Tree Adjoning Grammar. Master's thesis, University 
of Pennsylvania. 
\[Fukui and Speas, 1986\] Naoki 
Fukui and Margaret Speas. Specifiers and projec- 
tion. In Naold Fukui, T. Rappaport, and E. Sagey, 
editors, MIT Working Papers in Linguistics 8, MIT 
Department of Linguistics. 
\[Johnson, 1988\] Mark Johnson. Parsing as deduction: the 
use of knowledge of language. In The MIT Parsing 
Volume, 1987-88, MIT Center for Cognitive Science. 
\[Joshi, 1985\] Aravind Joshi. How much context- 
sensitivity is required to provide reasonable structural 
descriptions: tree adjoining grammars. In D. Dowty, 
L. Kartunnen, and A. Zwicky, editors, Natural Lan- 
guage Processing: Psycholinguistic, Computational 
and Theoretical Perspectives, Cambridge University 
Press. 
\[Kroch, 1986\] Anthony Kroch. Unbounded dependencies 
and subjacency in a tree adjoining grammar. In A. 
Manaster-Ramer, editor, The Mathematics of Lan- 
guage, John Benjamins. 
\[Kroeh, 1987\] Anthony Kroch. Assymetries in long 
distance extraction in a tree adjoining grammar. 
manuscript, University of Pennsylvania. 
\[Kroch and Joshi, 1985\] Anthony Kroch and Aravind 
Joshi. The Linguistic Relevance of Tree Adjoining 
Grammar. Technical Report MS-CS-85-16, Univer- 
sity of Pennsylvania Department of Computer and 
Information Sciences. To appear in Linguistics and 
Philosophy. 
\[Kroch and Santorini, 1987\] Anthony Kroch and Beatrice 
Santorini. The derived constituent structure of the 
118 
west germanic verb raising construction. In R. Frei- 
din, editor, Proceedings of the Princeton Conference 
on Comparative Grammar, MIT Press, Cambridge, 
MA. 
\[Marcus, 1980\] Mitchell Marcus. A Theory of Syntactic 
Recognition for Natural Language. MIT Press, Cam- 
bridge, MA. 
\[Schabes et al., 1988\] Yves Schabes, Anne Abeill6, and 
Aravind K. Joshi. Parsing strategies with 'lexical- 
ized' grammars: application to tree adjoining gram- 
mars. In COLING Proceedings, Budapest. 
\[Stabler, 1990\] Edward Stabler. Implementing govern- 
ment binding theories. In Levine and Davis, ed- 
itors, Formal Linguistics: Theory and Implementa- 
tion. forthcoming. 
