SYNTACTIC/SEMANTIC COUPLING IN THE BBN DELPHI 
SYSTEM 
Robert Bobrow, Robert Ingria, David Stallard 
BBN Systems and Technologies 
10 Moulton Street 
Cambridge, MA 02138 
ABSTRACT 
We have recently made significant changes to the BBN DELPHI 
syntactic and semantic analysis component. These goal of these 
changes was to maintain the fight coupling between syntax and se- 
mantics characteristic of earlier versions of DELPHI, while making 
it possible for the system to provide useful semantic interpreta- 
tions of input for which complete syntactic analysis is impossi- 
ble. Semantic interpretation is viewed as a process operating on 
a sequence of messages characterizing local grammatical relations 
among phrases, rather than as a recursive tree walk over a globaUy 
complete and coherent parse tree. The combination of incremental 
semantic interpretation and statistical control of the parsing pro- 
cess makes it feasible to reconstruct local grammatical relations 
with substantial accuracy, even when a global parse cannot be ob- 
tained. Grammatical relations provide the interface between syn- 
tactic processing and semantic interpretation, and standard global 
parsing is viewed as merely one way to obtain evidence for the ex- 
istence of such grammatical relations in an input string. This focus 
on grammatical relations leads to substantial simplification of both 
grammar and semantic rules, and will facilitate our ultimate aim 
of acquiring syntactic, semantic and lexical knowledge by largely 
automatic means. 
1. THE PROBLEM 
There are two long standing problems of computational lin- 
guistics for systems which do syntactic processing before 
semantic processing. First, they are limited by the cover- 
age of their grammar. Second, "syntactically ill-formed" 
word sequences, which represent a noticeable fraction of 
the utterances in any natural setting (e.g. spontaneously 
spoken input or unedited text) cause many failures. Other 
architectures have their own problems. Systems that depend 
primarily on semantic processing tend to be uncomfortably 
domain-dependent, working best in highly constrained prob- 
lem domains. "Semantic grammar" systems often do not 
capture a wide range of syntactic variations with the same 
meaning, while "frame based" systems typically allow for 
ill-formedness and syntactic variation by forcing all input 
into the procrustean bed of a highly limited task model. 
311 
2. DELPHI'S APROACH 
Our goal in the DELPHI system has to develop techniques 
that allow general, task independent, syntactic knowledge to 
be used to the fullest extent possible, making it feasible to 
encode semantic knowledge in the simplest (and thus most 
learnable) form, without sacrificing generality. Classical ap- 
proaches to this ideal fail completely when presented with 
"syntactically ill-formed" input, whether that ill-formedness 
is due to the system's incomplete representation of syntac- 
tic regularities, or to genuine disfluencies on the part of the 
speaker/writer. We have been continually making progress 
toward a balanced approach that allows us to take advan- 
tage of syntactic constraints wherever possible, while allow- 
ing the system to interpret inputs for which no grammatical 
parse can be found. 
The differences between our approach and more standard 
syntactically oriented approaches are subtle. At first glance, 
our grammar and our parser do not look radically different 
than thosed used in other syntactic analysis approaches (with 
the exception of the scheduling algorithm mentioned below). 
We started with a relatively standard context-free parsing 
algorithm, applied to what is for the most part a straightfor- 
ward unification-based grammar. The largest modification 
to the parser was its conversion to an agenda-based chart- 
pax. er, with scheduling depending on measured statistical 
likelihood of grammatical rules \[1\]. This enhanced effi- 
ciency significantly, by allowing us to generate parses in a 
"best first" order, but did not change the syntactic coverage 
of our system. 
All versions of DELPHI for the last several years have inte- 
grated semantic processing with parsing. This ensures that 
all syntactic structures placed in the chart are semantically 
coherent, further reducing the search space for the best parse. 
In the early versions of DELPHI, each syntactic rule had an 
associated semantic rule which had to be sucessfuUy applied 
before the syntactically hypothesized constituent would be 
accepted in the chart. Because of the large number of syn- 
tactic rules needed to have a broad coverage grammar, the 
number of semantic rules was quite large, and the represen- 
tation for lexical semantics was quite complex. 
2.1 Parsing as Transduction 
The biggest change in DELPHI came as we started to look at 
the parser, not as a device for constructing syntactic trees, 
but as an information txansducer that makes it possible to 
simplit 3, and generalize the roles for semantic interpretation. 
The purpose of syntactic analysis in this view is to make 
information encoded in ordering and constituency as readily 
available as the information encoded in the lexicai items, 
and to map syntactic paraphrases to informarionally equiv- 
alent structures. The actual interface between parsing and 
semantics is a dynamic process structured as a cascade (as 
in Woods notion of cascaded ATNs \[4\]), with parsing and 
semantic interpretation acting as coroutines. The input to 
the semantic interpreter is a sequence of messages, each re- 
questing the "binding" of some constituent to a head. The 
semantic interpreter does not perform any sort of recursive 
tree-walk over the syntactic structure produced by the parser, 
and is in fact immune to many details of the tree structure. 
This view of a grammar as a transducer between input strings 
and semantic representation made it possible for us to sub- 
stanriaily restructure the grammar in such a way as to both 
decrease the number of rules and increase its coverage. The 
original DELPHI grammar contained 1143 rules. The re- 
structured grammar has only 453 rules. This overall number 
perhaps underestimates the impact of the change in point of 
view, because it includes rules for various specialized sub- 
grammars such as numbers, latitudes and longitudes and 
clock times, which were not revised. The number of VP 
rules (excluding conjunction and modal rules) dropped from 
83 to 15, while the coverage of VP phenomena increased. 
2.2 The "Piece Parts" Metaphor 
In general, while certain orderings of modifiers seem 
strongly constrained by grammar (determiner and relative 
clause for NPs, subject and object for clauses, indirect ob- 
ject and object for VPs), other orderings seem to be more 
weakly determined (the "arguments" of a verb, such as the 
origin and destination phrases of a verb of motion, usu- 
ally occur before more general verbal adjuncts like rime- 
modifiers), and can be over-ridden by factors such as such 
as "heaviness". Thus, most attachments can be modelled by 
simple binary adjunction. Since the exact topology of the 
parse tree could be modified without materially affecting the 
transduction operation, we opted to generate complex recur- 
sive structures primarily by left and fight adjunction, using 
rules of the general form 
(X . . .) => (X-LEFT-MOD . . .) (X . ..) 
and 
(X ...) => (X ...) (X-RIGHT-MOD ...) 
312 
When the structures produced by such rules are written in a 
bracketed notation the resulting items look like notMng so 
much as onions! 
(((((x...) 
°°°) 
(X-RIGHT-MOD ...)) ...)...) 
The critical issue for a transducer, however, is the infor- 
marion flow from syntax to semantics, and at this level the 
layers of the onion disappear, with each adjunct being "log- 
ically attached" to the "head" of the constituent. 
In effect, we have factored a number of constructions that 
were previously treated as units into "piece parts" that can 
be combined together in various ways, subject to semantic 
well-formedness. Verb subcategorization is a prime example 
of one such area \[2\]. Rather than using subcategorization 
features to name sets of categories that appear together as 
complements, we have defined approximately 15 verb phrase 
rules that list the possible constituents that may appear as 
complements to a verb. These may embed witMn each other 
freely, so long as the results are semantically interpretable 
by the head. 
We have adopted this approach throughout the grammar. 
For example, the complements and adjuncts that may ap- 
pear within a noun phrase are introduced by recursive NP 
rules, similar to the VP rules we have discussed here. This 
recursive scheme allows the piece part rules of the grammar 
to be combined together in novel ways, governed by the 
lexical semantics of individual words. The grammar writer 
does not need to foresee all possible combinations, as before. 
2.3 Examples 
For example, in an earlier version of the grammar, in order 
for a verb, like "fly", to take two prepositional phrase com- 
plements, the following rule was required (for the purposes 
of exposition we suppress complex unification structures) : 
(VP ... :SUBJ :WFF) => 
(V :WORD ... 
(DITRANSPREP :PREP :PREP1 
:PP2) ...) 
(PP :PREP ... :PPi) 
(PP :PREP1 ... :PP2) 
•.. :PPi 
The word "fly" contained the feature (DITRANSPREP 
(FROMPREP) (TOPREP) ...) in its lexical entry to constrain 
the prepositions to be "from" and "to". In order for "fly" 
to take the prepositions in the opposite order, as well, either 
the lexical entry for "fly" would have contained the addi- 
tional subcategorization entry (DITRANSPREP (TOPREP) 
(FROMPREP) ...) with the values of the two preposition 
arguments reversed, or we would have needed to add the 
following "lexical redundancy rule" to the grammar: 
(VP ... :SUBJ :WFF) => 
(V :WORD ... 
(DITRANSPREP :PREP :PREP1 ... 
:PP2) ...) 
(PP :PREP1 ... :PP2) 
(PP :PREP ... :PPi) 
:PPi 
which automatically inverts the order of :PREP and :PREP1 
for any verb taking two prepositional phrases. To allow 
"fly" to take a "to" phrase without a "from" phrase, as often 
occurs, we would need both another subcategorization spec- 
ification in the lexical entry for "fly", and another rule in the 
grammar, allowing a verb to take a single PP complement. 
In the current grammar, all PP complements are handled by 
one single rule: 
(VP :AGR ...) => 
:HEAD (VP :AGR ...) 
:PP-COMP (PP :PREP ...) 
This rule allows a verb to take a single prepositional phrase 
complement, or, by recursion, an indefinite number of oth- 
ers, consistent with its semantics. The lexical information 
particular to individual verbs governs the number of prepo- 
sitions that the verb takes, their optionality or obligatoriness, 
and their semantic interpretation. Special purpose rules for 
different numbers and orders of PPs are not required. 
3. GRAMMATICAL RELATIONS 
The chief effort over the last year has been to codify this 
notion of logical attachment, simplifying the set of such 
attachments to highlight the common underlying substruc- 
ture of grammatical paraphrases. To this end we re-oriented 
our grammar around the notion of "grammatical relation". 
Grammatical relations include the familar ones of deep- 
structure subject and object, as well as other relations. These 
relations may be seen as the end result of making the in- 
formation encoded in ordering and constituency explicitly 
available. (In languages with freer word order this informa- 
tion is often encoded in morphological affixes or pre and post 
positions.) From the point of view of a syntactic-semantic 
transducer, the key point of any grammatical relation is that 
it licenses (one of) a small number of semantic relations be- 
tween the ("meanings" of) the related constituents. Some- 
times the grammatical relation constrains the semantic rela- 
tion in ways that cannot be predicted from the semantics of 
the constituents alone (given "John", "Mary" and "kissed", 
only the grammatical relations or prior world knowledge de- 
termine who gave and who received). Other times the gram- 
matical relation simply licenses the one plausible semantic 
relation (given "John", "ate" and "hamburger", if there is 
a relation, it is the hamburger that is most likely to have 
been consumed--but in the sentence "John ate the fries but 
rejected the hamburger" our knowledge of the destiny of the 
hamburger is mediated by its lack of a grammatical relation 
to "ate"). 
Grammatical relations are incorporated into the grammar by 
giving each element of the right hand side of a grammar rule 
a grammatical relation as a label. Some typical rules are, in 
schematic form: 
(NP ... ) => 
:tread (NP ...) 
:PP-COMP (PP :PREP ...) 
(N-BAR ...) => 
:PRE-NOM (N ...) 
: HZAD (N-BAR ... ) 
The first indicates that an NP may have an NP as a head 
and a PP as an adjunct, with the grammatical relation :pp- 
comp holding between them (the actual operation of binding 
a :pp-comp splits it into a number of sub-relations based on 
the preposition, but that can be safely ignored here). The 
second indicates that the head need not occur as the first con- 
stituent on the right side. All that is required is that one of 
the right-hand elements is labeled as the "head" of the rule, 
and it is the source of information about the initial semantic 
and syntactic "binding state". This binding state controls 
whether or not the other elements of the right-hand side can 
"bind" to the head via the relation that labels them. Seman- 
tics is associated with grammatical relations, not with par- 
ticular grammar rules (as are Montague-grammar and most 
unification-based semantics systems). 
3.1"Binding rules" - the Semantics of Gram- 
matical Relations 
The implementation of the use of such binding states in 
the transduction of grammatical relations to semantic struc- 
ture is facilitated by the procedural elements we have in- 
troduced into our unification grammar formalism. In early 
versions of DELPHI, grammar rules contained only unifi- 
cation expressions for grammatical constituents. Later ver- 
sions added "logical nodes"---expressions which looked like 
constituents, but which were satisfied by deductions in a 
unification-based axiom set. These logical nodes were used 
to add various constraints to the grammar, much as in a 
definite clause grammar. An analysis of the time spent in 
313 
parsing showed that substantial time was spent in such log- 
ical computations, and it became clear that more efficient 
data structures and procedural techniques could be used to 
implement many such computations \[3\]. The current version 
of the system uses these embedded procedural mechanisms 
to manipulate specialized data structures that efficently rep- 
resent the binding state of a constituent, to determine ff a 
proposed grammatical relation leads to a consistent bind- 
ing state, and if so what the semantic implications of that 
binding are. 
A separate system of "binding rules" for each grammatical 
relation licenses the binding of a constituent to a head via 
that relation by specifying the semantic implications of bind- 
ing. These rules generally specify aspects that must be true 
of the semantic structure of the head and bound constituent 
in order for the binding to take place, and may also specify 
certain syntactic requirements. They may take into account 
the existence of previous bindings to the head, allowing cer- 
tain semantic roles (such as time specification) to be frilled 
multiply, while other semantic roles may be restricted to 
having just one ffiller. 
As adjuncts are added to a structure the binding list is ex- 
tended. As layers are added to the onion, a simple linear 
list of bindings is maintained representing the head and its 
grammatical relation to each of the constituents added with 
each layer of the onion. Semantic binding rules are used to 
verify the local semantic plausibility of a structure, i.e. the 
semantic plausibility of each proposed grammatical relation. 
The next phase of semantic interpretation takes place when 
the onion is complete, i.e. when a constituent X is inserted 
as other than the head of a larger constituent. This situation 
provides evidence that the outermost layer of the onion has 
been reached, and that no more adjuncts are to be added. 
At this time it is possible to evaluate semantic rules that 
check for completeness and produce an "interpretation" of 
the constituent. These completion rules operate directly on 
the binding list, not on the recursive left or right branching 
tree structure produced by direct application of the grammar. 
The actual tree structure is at this level immaterial, having 
been replaced by the flattened binding list representation of 
relational structure. 
4. ROBUSTNESS BASED ON STATISTICS 
AND SEMANTICS 
Simply having a transduction system with semantics based 
on grammatical relations does not deal with the issue of ro- 
bustness - the ability to make sense of an input even if it can- 
not be assigned a well-formed syntactic tree. The difficulty 
with standard syntactic techniques is that local syntactic ev- 
idence is not enough to accurately determine grammatical 
relations. A NP (e.g. "John") followed by a verb (e.g. 
"flew") may be the subject of that verb (e.g. "John flew to 
314 
Boston") or may be unrelated (e.g. "The man I introduced to 
John flew to Boston"). The standard way of getting around 
this is to attempt to find a globally consistent set of gram- 
matical relation labels (i.e. a global parsed and make use 
of the fact that the existence of a global parse containing a 
given relation is stronger evidence for that relation than lo- 
cal structure (although syntactic ambiguity makes even such 
global structures suspect). This is indeed the best approach 
if all you have available is a syntactic grammar. 
The strategy we use in DELPHI is based on the existence of 
two other sources of information. In the first place we have 
semantic constraints that can be applied incrementally, so 
that we can check each proposed grammatical relation for se- 
mantic coherence in the context of other assumed grammat- 
ical structures. Additionally, we have statistical information 
on the likelihood of various word senses, grammatical rules, 
and grammatical-semantic transductiuns. Thus we can not 
only rule out many locally possible grammatical relations on 
the basis of semantic incoherence, we can rank alternative 
local structures on the basis of empirically measured statis- 
tics. The net result is that even in the absence of a single 
global parse, we can be reasonably sure of the local gram- 
matical relations and semantic content of various fragments 
(we can even give numerical estimates of the likelihood of 
each such structure). 
4.1 Control structure 
The DELPHI system attempts to obtain a complete parse of 
its input, using its agenda-based best-first parsing algorithm. 
If it is unable to do this it uses the parser in a fragment- 
production mode, producing the most probable structure for 
an initial segment of the input, then restarting the parser in 
a top down mode on the first element of the unparsed string 
whose lexical category provides a reasonable anchor for top- 
down prediction. This process is repeated until the entire 
input is spanned with fragments. Experiments have shown 
that the combination of statistical evaluation and semantic 
constraints lets this procedure produce a highly useful chunk- 
ing of the input for interpretation by other non-syntactically 
driven strategies. Further details are given in the accompa- 
nying paper on the DELPHI fall-back processing strategies. 
5. ADVANTAGES OF THIS APPROACH 
The separation of syntactic grammar rules from semantic 
binding and completion rules has important consequences 
for processing. First, it enables the notion of grammatical 
relation to be separated from the notion of tree structure, and 
thus greatly facilitates fragment parsing. Second, while it 
allows syntax and semantics to be strongly coupled in terms 
of processing (parsing and semantic interpretation) it allows 
them to be essentially decoupled in terms of notation. This 
makes the grammar and the semantics considerably easier 
to modify and maintain. 
We believe, however, that in the long term the most im- 
portant advantage is that this view leads us to a new kind 
of language model, in which knowledge can be much more 
easily extracted through automatic training. We view the 
role of the grammar as codifying the way that tree structure 
provides evidence for grammatical relations. Thus the rule 
(NP . . . ) => 
:HEAD (~ ...) 
:PP-COMP (PP :PREP ...) 
says that a noun phrase followed by a prepositional phrase 
provides evidence for the relation PP-COMP between the 
PP and NP head. 
The separation between rules types will allow us for the 
first time to consider the effect of grammatical relations on 
meaning, independently of the way that evidence for these 
relations is produced by the parser. One effect of this is to 
make it possible to use a hypothesized semantic interpreta- 
tion of a set of tree fragments to generate a new syntactic 
rule. 
Thus, in normal operation, the primary evidence for a gram- 
matical relation is the result of actually parsing part of an 
input. However, since grammatical relations between con- 
stituents entail semantic relations, if we can make an esti- 
mate of the likelihood of certain semantic relations based on 
domain knowledge, pragmatics, and task models, etc., it is 
in principle possible to use abductive reasoning to suggest 
likely grammatical relations, and thereby propose new gram- 
mar mles. In effect, grammatical relations form an abstract 
level of representation that greatly simplifies the interaction 
of syntactic and semantic processing. 
ACKNOWLEDGEMENTS 
The work reported here was supported by the Advanced Re- 
search Projects Agency and was monitored by the Office 
of Naval Research under Contract No. N00014-89-C-0008. 
The views and conclusions contained in this document are 
those of the authors and should not be interpreted as neces- 
sarily representing the official policies, either expressed or 
implied, of the Defense Advanced Research Projects Agency 
or the United States Government. 
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