TECHNICAL CORRESPONDENCE 
PARSING DISCONTINUOUS CONSTITUENTS IN 
DEPENDENCY GRAMMAR 
Discontinuous constituents--for example, a noun and its 
modifying adjective separated by words unrelated to them--- 
arc common in variable-word-order languages; Figure I 
shows examples. But phrase structure grammars, including 
ID/LP grammars, require each constituent to be a contigu- 
ous series of words. Insofar as standard parsing algorithms 
are based on phrase structure rules, they are inadequate for 
parsing such languagesJ 
The algorithm presented here, however, does not require 
constituents to be continuous, but merely prefers them so. 
It can therefore parse languages in which conventional 
parsing techniques do not work. At the same time, because 
of its preference for nearby attachments, it prefers to make 
constituents continuous when more than one analysis is 
possible. The new algorithm has been used successfully to 
parse Russian and Latin (Covington 1988, 1990). 
This algorithm uses dependency grammar. That is, in- 
stead of breaking the sentence into phrases and subphrases, 
it establishes links between individual words. Each link 
connects a word (the "head") with one of its "dependents" 
(an argument or modifier). Figure 2 shows how this works. 
The arrows point from head to dependent; a head can have 
many dependents, but each dependent can have only one 
head. Of course the same word can be the head in one link 
and the dependent in another. 2 
Dependency grammar is equivalent to an X-bar theory 
with only one phrasal bar level (Figure 3)--the dependents 
of a word are the heads of its sisters. Thus dependency 
grammar captures the increasingly recognized importance 
of headship in syntax. At the same time, the absence of 
phrasal nodes from the dependency representation stream- 
lines the search process during parsing. 
The parser presupposes a grammar that specifies which 
words can depend on which. In the prototype, the grammar 
consists of unification-based dependency rules (called 
D-rules) such as: 
"category:noun \] r category:verb\] person:X I 
number." Y I "~ |person:X | 
case:nominativeJ Lnumber:Y J 
This rule sanctions a dependency relation between any two 
words whose features unify with the structures shown--in 
this case, the verb and its subject in a language such as 
Russian or Latin. The arrow means "can depend on" and 
the word order is not specified. X and Y are variables. 
D-rules take the place of the phrase structure rules used by 
Shieber (1986) and others; semantic information can easily 
be added to them, and the whole power of unification-based 
grammar is available. 
The parser accepts words from the input string and keeps 
track of whether or not each word is "independent" (not yet 
known to depend on another word), indicated by + or - in 
Figure 4. On accepting a word W, the parser does the 
following: 
(1) Search the independent words (those marked +), 
mosl~ recent first, for words that can depend on W. If any 
are tbund, establish the dependencies and change the mark- 
ing of the dependents from + to -. 
(21) Search all words so far seen, most recent first, for a 
worct on which W can depend. If one is found, establish the 
dependency and mark Was -. Otherwise mark Was +. 
Figure 4 shows the process in action. The first three 
words, ultima Cumaei venit, are accepted without creating 
any links. Then the parser accepts iam and makes it depend 
on venit. Next the parser accepts carminis, on which Cu- 
maei, already in the list, depends. Finally it accepts aetas, 
which becomes a dependent of venit and the head of ultima 
and carminis. 
The most-recent-first search order gives the parser its 
preference for continuous constituents. The search order is 
significant because it is assumed that the parser can back- 
track, i.e., whenever there are alternatives it can back up 
and try them. This is necessary to avoid "garden paths" 
such as taking animalia (ambiguously nominative or accu- 
sative) to be the subject of animalia vident pueri "boys see 
animals." 
With ordinary sentences, however, backtracking is rela- 
tively seldom necessary. Further, there appear to be other 
constraints on variable word order. Ades and Steedman 
(1982) propose that all discontinuities can be resolved by a 
pushdown stack. (For example, pick up ultima, then Cu- 
maei, then put down Cumaei next to carminis, then put 
down ultima next to aetas. Crossing movements are not 
permitted.) Moreover, there appears to be an absolute 
constraint against mixing clauses together? If these hypoth- 
eses hold true, the parser can be modified to restrict the 
search process accordingly. 
Most dependency parsers have followed a "principle of 
adjacency" that requires every word plus all its direct and 
indirect dependents to form a contiguous substring (Hays 
and Ziehe 1960; Starosta and Nomura 1986; Fraser 1989; 
but not Hellwig 1986 and possibly not J/ippinen et al. 
1986). This is equivalent to requiring constituents to be 
continuous. This parser imposes no such requirement. To 
add the adjacency requirement, one would modify it as 
follows: 
(1) When looking for potential dependents of W, never 
234 Computational Linguistics Volume 16, Number 4, December 1990 
Technical Correspondence 
S 
VP NP 
A d~N 
Adj V Adv N 
I I I I 
ulll"nTa Cumael venil Jam corm\]his oelas 
last Cumean has come now song age 
'The last era of the Cumean song has now arrived' (Latin; Vergil. Eclogues IV.d) 
S 
NP Aux NP V 
i i 
kurdu-ngku ka mahdi waj\])~lll"-nyi wila-ngku 
child dog chase small 
'The small child is chasing the dog' 
(Warlpiri; Siewierska 1988:158. citing Nash) 
Figure I. Examples of discontinuous constituents. 
).he big dog chased Lhe cat 
Figure 2. Dependency representation of a 
scntence. Arrows point from each word to its 
depcndents (modifiers or arguments). 
A B C D 
D 
A B C D 
Figure 3. Equivalence of dependency network to 
X-bar tree. 
I. 
2. 
ulLllrn a + 
ul\[Zrna Curnael + + 
3. uHima Cumaei venil 
÷ + 4- 
4. u/lima Cumaei veni\[ iam + + + -- 
5. ultZrna Cumael venH ibm carmmis 
+ -- + -- 4- 
ulHma Cumaei venH ibm carmimL~ aeLas 
Figure 4. The parser accepts words one by one and 
tries to link them together; ' +' marks words that 
do not (yet) depend on other words. 
skip over an independent word. That is, if an independent 
word is found that cannot depend on IV, then neither can 
any earlier independent word. 
(2) When looking for the word on which W depends, 
consider only the previous word, that word's head, the 
head's head if any, and so on. 
With these requirements added, the algorithm would be 
the same as one implemented by Hudson (1989). 
Formal complexity analysis has not been carried out, but 
my algorithm is simpler, at least conceptually, than the 
variable-word-order parsers of Johnson (1985), Kashket 
(1986), and Abramson and Dahl (1989). Johnson's parser 
and Abramson and Dahl's parser use constituency trees 
with explicit discontinuity ("tangled trees"), with all their 
inherent unwieldiness. Kashket's parser, though based on 
GB theory, is effectively a dependency parser since it relies 
on case assignment and subcategorization rather than tree 
structure. 
Michael A. Covington 
Artificial Intelligence Programs 
The University of Georgia 
Athens, GA 30602 
NOTES 
1. The early stages of this work were supported by National Science 
F;oundation grant IST-85-02477. I am grateful to Norman Fraser and 
Pdchard Hudson for comments and encouragement. 
2. On dependency grammar in general see Tesni6re 1959, Hays 1964, 
Robinson 1970, Hudson 1986, Schubert 1987, Mel'~uk 1988, and 
Starosta 1988. In Hudson's system, a single word can have two heads 
provided the grammatical relations connecting it to them are distinct. 
3. As pointed out by an anonymous reviewer for Computational Linguis- 
tics. 

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