Coping with Extragrarnmaticality 
Jalme G. Carbonell and Philip J. Hayes 
Computer Science Department, Carnegie-Mellon University 
Pittsburgh, PA 15213. USA 
Abstract 1 
Practical natural language interfaces must exhibit robust 
bei~aviour in the presence of extragrammaticat user input. This 
paper classifies different types of grammatical deviations and 
related phenomena at the lexical and sentential levels, 
discussing recovery strategies tailored to specific phenomena 
in the classification. Such strategies constitute a tool chest of 
computationally tractable methods for coping with 
extragrammaticality in restricted domain natural language. 
Some of the strategies have been tested and proven viable in 
existing parsers. 
1. Introduction 
Any robust natural language interface must be capable of 
processing input utterances that deviate from its grammatical and 
semantic expectations. Many researchers have made this 
observation and have taken initial steps towards coverage of 
certain classes of extragrammatical constructions. Since robust 
parsers must deal primarily with input that does meet their 
expectations, the various efforts at coping with 
extragrammaticality have generally been structured as extensions 
to existing parsing methods. Probably the most popular approach 
has been to extend syntactically.oriented parsing techniques 
employing Augmented Transition Networks (ATNs) 
\[21, 24, 25, 29\]. Other researchers have attempted to deal with 
ungrammatical input through network-based semantic grammar 
techniques \[19. 20j. through extensions to pattern matching 
parsing in which partial pattern matching is allowed \[16\], through 
conceptual case frame instantiafion \[12, 22\], and through 
approaches involving multiple cooperating parsing strategies 
\[7, 9, 18\]. 
Given the background of existing work, this paper focuses on 
two major objectives: 
1. to create a taxonomy of grammatical deviations covering a 
broad range of extragrammaticalities, 
2. to outline strategies for processing many of these deviations, 
3. to assess how easily these strategies can be employed in 
conjunction with existing parsing methods. 
The overall result should be a synthesis of different parse. 
recovery strategies organized by the grammatical phenomena 
they address (or violate), an evaluation of how well the strategies 
• integrate with existing approaches to parsing extragrammatical 
1This research was sponsored in part by the Air Force Office of Scientific 
Research under Contract AFOSR-82-0219 and in part by Digital Equipment 
Corporation as part of the XCALIBUR project. 
input, and a set of characteristics desirable in any parsing process 
dealing with extragrammatical input. We hope this will aid 
researchers designing robust natural language interfaces in two 
ways: 
t.by providing a tool chest of computationally effective 
approaches to cope with extragrammaticality; 
2. by assisting in the selection of a basic parsing methodology 
in which to embed these recovery techniques. 
In assessing the degree of compatibility between recovery 
techniques and various approaches to parsing, we will avoid the 
issue of whether a given recovery technique can be used with a 
specific approach to parsing. The answer to such a question is 
almost always affirmative. Instead, we will be concerned with how 
naturally the recovery strategies fit with the various parsing 
approaches. In particular, we will consider the computational 
tractability of the recovery strategies and how easily they can 
obtain the information they need to operate in the context of 
different parsing approaches. 
Extragrammaticalities include patently ungrammatical 
constructions, which may nevertheless be semantically 
comprehensible, as well as lexical difficulties (e.g. misspellings), 
violations of semantic constraints, utterances that may be 
grammatically acceptable but are beyond the syntactic coverage 
of the system, ellipsed fragments and other dialogue phenomena, 
and any other difficulties that may arise in parsing individual 
utterances• An extragrammaticality is thus defined with respect to 
the capabilities of a particular system, rather than with respect to 
an absolute external competence model of the ideal speaker. 
Extragrammaticality may arise at various levels: lexical, sentential, 
and dialogue. This paper addresses the first two categories; the 
third is discussed in \[8, 11\]. Our discussions are based on direct 
experience with various working parsers: FLEXP, CASPAR and 
DYPAR \[7, 8, 16\]. 
2. Lexical Level Extragrammaticalities 
One of the most frequent parsing problems is finding an 
unrecognizable word in the input stream. The following sections 
discuss the underlying reasons for the presence of 
unrecognizable words and describe suitable recovery strategies. 
2.1. The unknown word problem 
The word is a legitimate lexeme but is not in the system's 
dictionary. There are three reasons for this: 
• The word is outside the intended coverage of the interface 
(e.g. There is no reason why a natural language interface to 
an electronic mail system should know words like "chair" or 
"sky", which cannot be defined in terms of concepts in its 
semantic domain). 
437 
o The word refers to a legitimate domain concept or 
combination of domain concepts, but was not included in the 
dictionary. (e.g. A word like "forward" \[a message\] can be 
defined as a command verb, its action can be clearly 
specified, and the objects upon which it operates- an old 
message and a new recipient -- are already well-formed 
domain concepts.) 
• The word is a proper name or a unique identifier, such as a 
catalogue part name/number, not heretofore encountered by 
the system, but recognizable by a combination of contextual 
expectations and morphological or orthographic features 
(e.g., capitalization). 
In the first situation, there is no meaningful recovery strategy 
other than focused interaction\[15\] to inform the user of the 
precise difficulty. In the third, little action is required beyond 
recognizing the proper name and recording it appropriately for 
future reference. The second situation is more complicated; three 
basic recovery strategies are possible: 
1. Follow the KLAUS\[14\] approach where the system 
temporarily wrests initiative from the user and plays a well 
designed "twenty questions" game, classifying the unknown 
term syntactically, and relating it semantically to existing 
concepts encoded in an inheritance hierarchy. This method 
has proven successful for verbs, nouns and adjectives, but 
only when they turn out to be instances of predefined general 
classes of objects and actions in the domain model. 
2. Apply the project and integrate method \[6\] to infer the 
meaning and syntactic category o.f the word from context. 
This method has proven useful for nouns and adjectives 
whose meaning can be viewed as a recombination of features 
present elsewhere in the input. Unlike the KLAUS method, it 
operates in the background, placing no major run-time 
burden on the user. However, it remains highly experimental 
and may not prove practical without user confirmation. 
3. Interact with the user in a focused manner to provide a 
paraphrase of the segment of input containing the unknown 
word. If this paraphrase results in the desired action, it is 
stored and becomes the meaning of the new word in the 
immediate context in which it appeared. The LIFER system 
\[20\] had a rudimentary capacity for defining synonymous 
phrases. A more general method would distinguish between 
true synonymy and functional equivalence in order to classify 
the new word or phrase in different semantic contexts. 
2.2. Misspellings 
Misspellings arise when an otherwise recognizable lexeme has 
letters omitted, substituted, transposed, or spuriously inserted. 
Misspellings are the most common form of extragrammaticality 
encountered by natural language interfaces. Usually, a word is 
misspell into an unrecognizable character string. But, 
occasionally a word is misspelt into another word in the dictionary 
that violates semantic or syntactic expectations. For instance: 
Copy the flies from ~he accounts direc!ory to my airectory 
Although "flies" may be a legitimate word in the domain of a 
particular interface (e.g., the files coulcJ consist of statistics on 
med-flv infestation in California). it is obvious to the human reader 
that there is a misspelling in the sentence above. 
There are well-known algorithms for matching a misspelt word 
against a set of possible corrections \[13\]. and the simplest 
recovery strategy is to match unknown words against the set of all 
words in an interface's dictionary. However, this obviously 
produces incorrect results when a word is misspell into a word 
already in the dictionary, and can produce unnecessary 
ambiguities in other cases. 
Superior results are obtained by making the spelling correction 
sensitive to the parser's syntactic and semantic expectations. In 
the following example: 
Add two fixed haed dual prot disks to the order 
"haed" can be corrected to: "had", "head", "hand:', "heed", and 
"hated". Syntactic expectations rule two of these out, and 
domain semantics rule out two others, leaving "fixed \[lead disk" 
as the appropriate correction. Com\[;utationally, there are two 
ways to organize this. One can either match parser expectations 
against all possible corrections in the parser:s current vocabulary, 
and rule out spurious corrections, or one can use the parse 
expectations to generate a set of possible words that can be 
recognized at the present point and use this as input to the 
spelling correction algorithm. The latter, when it can be done, is 
clearly the preferable choice on efficiency criteria. Generating all 
possible corrections with a 10,080 word dictionary, only to rule out 
all but one or two, is a computationally-intensive process, whereas 
exploiting fully-indexed parser expectations is far more 
constrained and less likely to generate ambiguity. For the 
example abcve, "pror' has 16 possible corrections in a small on- 
line dictionary. However, domain semantics allow only one word 
in the same position as "pror', so correction is most effective if 
the list of possible words is generated first. 
2.3. Interaction of morphology and misspelling 
Troublesome side.effects of spelling correction can arise with 
parsers that have an initial morphological analysis phase to 
reduce words to their root form. For instance, a parser might just 
store the root form of 'directory' and reduce 'directories' to 
'directory' plus a plural marker as part of its initial morphological 
phase. This process is triggered by failing to recognize the 
inflected form as a wind that is present in the dictionary. It 
operates by applying standard morphological rules 
(e.g. -tes => +,y) to derive a root from the inflected form. It a 
simple matter to check first for inflected forms and then for 
misspellings. However, if a word is both inflected and misspelt, 
the expectation-based spelling correcter must be invoked from 
within the morphological decomposition routines on potentially 
misspelt roots or inflexions. 
2.4. Incorrect segmentation 
Input typed to a natural language interface is segmented into 
words by spaces and punctuation marks. Both kinds of 
segmenting markers, especially the second, can be omitted or 
inserted speciously. Incorrect segmentation at the lexical level 
results in two or more words being run together, as in 
"runtogether", or a single word being split up into two or more 
segments, as in "tog ether" or (inconveniently) "to get her", or 
combinations of these effects as in "runto geth er". In all these 
cases, it is possible to deal with such errors by extending the 
spelling correction mechanism to be able to recognize target 
words as initial segments of unknown words, and vice.versa. 
Compound errors, however, present some difficulties. For 
instance consider the following example where we have both a 
missing and a spurious delimiter: 
Add two du alport disks to the order 
After failing in the standard recovery methods, one letter at a time 
would be stripped off the beginning of the second unrecognizable 
word ("alporr') and added at the end of the first unrecognizable 
word ("du"). This process succeeds only if at some step both 
words are recognizable and enable the parse to continue. 
Migrating the delimiter (the space) backwards as well as forwards 
should also be attempted between a pair of unknown words, 
438 
stopping if both words become recognizable. Of course, 
additional compounding of multi.hie iexical deviations (e.g., 
misspellings, run-on words and split words in the same segment) 
requires combinatorially inefficient recovery strategies. Strong 
parser expectabons can reduce the impact of this problem, but at 
some point tradeoffs must be made between resilience and 
efficiency in compound error recovery. 
3. Sentential Level Extragrammaticalities 
We examine ungrammaticalities at the sentential level in five 
basic categories: missing words, spurious words or phrases, out 
of order constituents, agreement violations, and semantic 
constraint violations. 
3.1. Missing constituents 
It is not uncommon for the use; of a natural language interface 
to omit words from his input. The degree of recovery possible 
from such ungrammaticalities is, of course, dependent on which 
words were left out. In practice, words whose contribution to the 
sentence is redundant are often omitted in an attempt to be cryptic 
or "computer-like" (as in "Copy new files my directory"). This 
suggests that techniques that fill in the structural gaps on 
semantic grounds are more likely tobe successful than strategies 
which do not facilitate the application of oor,~ain semantics. 
A parsing process postulates a missing word error when its 
eYpectations (syntactic or semantic) of what should go at a certain 
place in the input utterance are violated. To discover that the 
problem is in fact a missing word, and to find the parse structure 
corresponding to the user's intention, the parsing process must 
"step back" and examine the context of the parse as a whole. It 
needs to ignore temporarily the unfulfilled expectations and their 
contribution to the overall structure while it tries to fulfil some of its 
other expectations through parsing other parts of the input and 
integrating them with already parsed constituents. More 
specifically, the parser needs to delimit the gap in the input 
utterance, correlate it with a gap in the parse structure (filling in 
that ga~ if it is uniquely determined), and realign the parsing 
mechanism as though the gap did not exist. Such a realignment 
can be done top-down by predicting the other constituents from 
the parse structure already obtained and attempting to find them 
in the input stream. Alternatively, realignment can be done 
bottom-up by recognizing as yet unparsed elements of the input, 
and either fitting them into an existing parse structure, or finding a 
larger structure to subsume both them and the existing structure. 
This latter approach is essential when the structuring words are 
missing or garbled. 
3.2. Spurious and unrecognizable constituents 
Words in an input utterance that are spurious to a parse can 
arise from a variety of sources: 
• legitimate phrases that the parser cannot deal with: It 
is not uncommon for the user of a restricted domain interface 
to say things that the interface cannot understand because of 
either conceptual or grammatical limitations. Sometimes, 
spurious verbosity or politeness is involved: 
Add if you would be so kind two fixed head and if possible 
dual ported disks to my order. 
Or the user may offer irrelevant (to the system) explanations 
or justifications, as observed in preparatory experiments for 
the GUS system \[4\], e.g. 
/ think / need more storage capacity, so add two fixed head 
dual ported disks to my order. 
Some common phrases of politeness can be recognized 
explicitly, but in most cases, the only reasonable response is 
to ignore the unknown phrases, realign the parse on the 
recognizable input, and if a semantically and syntactically 
complete structure results, postulate that the ignored 
segment was indeed redundant. Isolating certifiable noise 
phrases in the same way as truly spurious input provides the 
advantage that they can then be recognized at any point in 
the input without having to clutter the parser's normal 
processing with expectations about where they might occur. 
• broken-off and restarted utterances: These occur when 
people start to say one thing, change their mind, and say 
another: 
Add I mean remove a disk from my order 
Utterances in this form are more likely to occur in spoken 
input but a similar effect can arise in typed input when a user 
forgets to hit the erase line or erase character key: 
Add remove a disk from my order 
Add a single ported dual ported disk from my order 
Again the best tactic is to discard the broken-off fragment, 
but identifying and delineating the superseded fragment 
requares strategies such as the one discussed below. 
• unknown words filling a known grammatical role: 
Sometimes the user will generate an incomprehensible 
phrase synonymous with a constituent the system is perfectly 
capable of understanding: 
Add a dual ported rotating mass storage device to my order 
Here the system might not know that "rotating mass storage 
device" is synonymous with "disk". This phenomenon will 
result in missing words as well as spurious words. If the 
system has a unique expectation for what should go in the 
gap, it should (with appropriate confirmation from the user) 
record the unknown words as synonymous with what it 
expected. If the system has a limited set of expectations for 
what might go in the gap, it could ask the user which one (if 
any) he meant and again record the synonym for future 
reference. In cases where there are no strong expectations, 
tile system would ask for a paraphrase of the 
incomprehensible fragment. If this proved comprehensible, it 
would then postulate the synonymy relation, ask the user for 
confirmation, and again store the results for future reference. 
As for missing constituents, recovery from spurious interjections 
generally requires "stepping back" and examining the context of 
the parse as a whole. In this case however, violations of the 
parser's expectations should result in skipping over the 
troublesome segments, and attempting to fulfill the expectations 
by parsing subsequent segments of tile input. If this results in a 
complete parse, the skipped segment may well be spurious. On 
the other hand, if a gap in the parse strdcture remains, it can be 
correlated with the skipped segments to postulate possible 
constituents an• synonomy relations as illustrated above. 
In the case of broken-off utterances, there are some more 
specific methods that allow the spurious part of the input to be 
detected: 
• If a sequence of two constituents of identical syntactic and 
semantic type is found where only one is permissible, simply 
ignore the first constituent. Two main command verbs in 
sequence (e.g., in the "Add remove ..." example above), 
instantiate the identical sentential case I~eader role in a case 
frame parser, enabling the former to be ignored. Similarly, 
two ,lstantiations of the same prencminal case for the "disk" 
case frame would be recognized as mutually incompatible 
and the former again ignored. Other parsing strategies can 
439 
be extended to recognize equivalent constituent repetition, 
but case frame instantiation seems uniquely well suited to it. 
• Recognize explicit corrective phrases and if the constituent 
to the right is of equivalent syntactic and semantic type as the 
constituent at the left, substitute the right constituent for the 
left constituent and continue the parse. This strategy 
recovers from utterances such as "Add I mean remove ...", if 
"1 mean" is recognized as a corrective phrase. 
• Select the minimal constituent for all substitutions. For 
instance the most natural reading of: 
Add a nigh speed tape drive, that's disk drive, to the order 
is to substitute "disk drive" for "tape drive", and not for the 
larger phrase "high speed tape drive", which also forms a 
legitimate constituent of like semantic and syntactic type. 
3.3. Out of order constituents and fragmentary input 
Sometimes, a user will employ non-standard word order. There 
are a variety of reasons why users violate expected constituent 
ordering relations, including unwillingness to change what has 
already been typed, especially when extensive retyping would be 
required: 
Two fixed head dual ported disk drives add to the order 
or a belief that a computer will understand a clipped pseudo- 
milita,~/style more easily than standard usage: 
two disk drives fixed head du~/ ported to my order add 
Similar myth~ about what computers understand best can lead to 
a very fragmented and cryptic style in which all function words are 
eliminated: 
Add disk drive order 
instead of "add a disk drive to my order". 
These two phenomena, out of order constituents and 
fragmentary input, are grouped together because they are similar 
from the parsing point of view. The parser's problem in each case 
is to put together a group of recognizable sentence fragments 
without the normal syntactic glue of function words or position 
cues to indicate how the fragments should be combined. Since 
this syntactic information is not present, semantic considerations 
have to shoulder the burden alone. Hence, parsers which make it 
easy for semantic information to be brought to bear are at a 
considerable advantage. 
Both bottom-up and top.down recovery strategies are possible 
for detecting and recovering from missing and spurious 
constituents. In the bottom-up approach, all the fragments are 
recognized independently, and purely semantic constraints are 
used to assemble them into a single framework meaningful in 
terms of the domain of discourse. When the domain is restricted 
enough, the semantic constraints can be such that they always 
produce a unique result. This characteristic was exploited to 
good effect in the PLANES system \[23\] in which an input utterance 
w~s recognized as a sequence of fragments which were then 
assembled into a meaningful whole on the basis of semantic 
considerations alone. A top-clown approach to fragment 
recognition requires that the top-level or organizing concept in the 
utterance ("add" in the above examples) be located, if it can be, 
the predictions obtainable from it about what else might appear in 
the utterance can be used to guide and constrain the recognition 
of the other fragments. 
As a final point, note that in the case of out of order constituents, 
a parser relying on a strict left-to-right scan will have much greater 
difficulty than one with more directional freedom. In out of order 
input, there may be no meaningful set of left-to-right expectations, 
even allowing for gaps or extra constituents, that will fit the input. 
For instance, a case frame parser that scans for the head of a case 
frame, and subsequently attempts to instantiate the individual 
cases from surrounding input, is far more amenable to this type of 
recovery than one whose expectations are expressed as word 
order constraints. 
3.4. Syntactic and semantic constraint violations 
Input to a natural language system can violate both syntactic 
and semantic constraints. The most.common form of syntactic 
constraint violation is agreement failure between subject and verb 
or determiner and head noun: 
Do the order include a disk drives? 
Semantic constraint violations can occur because the user has 
conceptual problems: 
Add a floating head tape drive to the order 
or because he is imprecise in his language, using a related object 
in place of the object he really means. For instance, if he is trying 
to decide on the amount of memory to include in an order he 
might say: 
Can you connect a video disk drive to the two megabytes? 
When what he-really means is "... to the computer with two 
megabytes of memory?.". 
These different kinds of constraint violation require quite 
different kinds of treatment. In general, the syntactic agreement 
violations can be ignored; cases in which agreement or lack of it 
distinguishes between two otherwise valid readings of an input are 
rare. However, one problem that sometimes arises is knowing 
whether a noun phrase is singular or plural when the determiner 
or quantifier disagrees with the head noun. 
Semantic constraint violations due to a user's conceptual 
problems are harder to deal with. Once detected, the only 
solution is to inform the user of his misconcepLion and let him take 
it from there. The actual detection of the problem, however, can 
cause some difficulty for a parser re!ymg heavily on semantic 
constraints to guide its parse. The constraint violation miOht 
cause it to assume there was some oth~r problem such as out of 
order or spurious constituents, and look for (and perhaps even 
find) some alternative and unintended way of putting all the pieces 
together. This is one case where syntactic considerations should 
come to the fore. 
Semantic constraint violations based on the mention of a related 
object instead of the entity actually intended by the user will 
manifest themselves in the same way as the semantic constraint 
violations based on misconceptions, but their processing needs to 
be quite different. The violation can be resolved if the system can 
look at objects related to the one the user mentioned and find one 
that satisfies the constraints. In the example above, this means 
going from the memory size to the machine that has that amount 
of memory. Clearly, the semantic distance and the type of 
relationship over which this kind of substitution is allowed needs 
to be controlled fairly carefully -- m a restricted domain everything 
is eventually related to everything e!se. Preference rules are 
needed to control the kind of substitutions that are allowed. In the 
above example, it might be that a part ~s allowed to substitute for a 
whole (metonymy), especially if, as we assumed, the part had been 
used earlier in the dialogue to distinguish between different 
instances of the whole. 
440 
4. Support for recovery strategies by various parsing approaches 
We now turn to the question of incorporating recovery strategies 
into some of the approaches to parsing found in the literature. We 
consider three basic classes: transition network approaches 
(including syntactic ATNs and network-based semantic 
grammars), pattern matching approaches, and approaches based 
on case frame instantiation. These classes cover the majority of 
current catsing systems for restricted domain languages. 
All three approaches are able to cope with lexical level problems 
satisfactorily. However, as we have seen, the application of 
semantic constraints often makes the correction of lexical 
problems more efficient and less prone to ambiguity. So parsers 
that employ semantic constraints (e.g. semantic grammars \[20, 5\] 
or case frame instantiation \[12, 17\]) are more effective in recovery 
at the lexical level than parsers whose only expectations are 
syntactic (e.g., purely syntactic ATNs \[28\]). At the sentential level, 
however, differences in the abilities of the three approaches to 
cope naturally with extragrammaticality are far more pronounced. 
We will examine each approach in turn from this point of view. 
4.1. Recovery strategies and transition network parsers 
Althou~jh attempts have been made to incorporate sentential 
level recovery strategies into network-based parsers including 
beth syntactically-based ATNs \[21,24, 25, 29\] and semantic 
grammar networks \[20\], the network paradigm itself is not well 
suited to the kinds of recovery strategaes discussed in the 
preceding sections. These strategies generally require an 
interpretive abdity to "step back" and take a broad view of the 
situation when a parser's expectations are violated, and this is 
very hard to do when using networks. The underlying problem is 
that a significant amount of state information during the parse is 
implicitly encoded by the position in the network; in the case of 
AThls, other aspects of the state are contained in the settings of 
scattered registers. As demonstrated by the recta-rule approach 
to diagnosing parse failures described by Weischedel and 
Sondheimer \[24\]. these and other difficulties elaborated below do 
not make recovery from extragrammaticality impossible. However, 
they do make it difficult and often impractical, since much of the 
implicitly encoded state must be made declarative and explicit to 
the recovery strategies. 
Often an ATN parse will continue beyond the point where the 
grammatical deviation, say an omitted word, occurred and reach a 
node in the network fiom which it can make no further progreSS 
(i.e., no arcs can be traversed). At this point, the parser cannot 
ascertain the source of th.~. ' error by examining its internal state 
even if the state is accessible -- the parser may have popped from 
embedded subnets, or followed a totally spurious sequence of 
arcs before blocking. If these problems can be overcome and the 
source of the error determined precisely, a major problem still 
remains: in order to recover, and parse input that does not accord 
with the grammar, while remaining true to the network formalism, 
the parser must modify the network dynamicall) and temporarily, 
and use the modified network to proceed through the present 
difficulties. Needless to say, this is at best a very complex process, 
one whose computational tractability is open to question in the 
most general case (though see \[21\]). It is perhaps not surprising 
that in one of the most effective recovery mechanisms developed 
for network-based parsing, the LIFER system's ellipsis handling 
routine \[20\], the key step operates completely outside the network 
formalism. 
As we have seen, semantic constraints are very important in 
recovering from many types of ungrammatical input, and these are 
by definition unavailable in a purely syntactic ATN parser. 
However, semantic information can be brought to bear on network 
based parsing, either through the semantic grammar approach in 
which joint semantic and syntactic categories are used directly in 
the ATN, or by allowing the tests on ATN arcs to depend on 
semantic criteria \[2, 3\]. In the former technique, the appropriate 
semantic information for recovery can be applied only if the 
correct network node can be located -- a sometimes difficult task 
as we have seen. In the latter technique, sometimes known as 
cascaded ATNs \[27\], the syntactic and semantic parts of the 
grammar are kept separate, thus giving the potential for a higher 
d~gree of interpretivem:ss in using the semantic information. 
However, semantic information represented in this fashion is 
generally only used to confirm or disconfirm parses arrived at on 
syntactic grounds and does not participate directly in the parsing 
process. 
A further disadvantage of the network approach for 
implementing flexible recovery strategies is that networks naturally 
operate in a top-down left-to-right mode. As we have seen, a 
bottom.up capability is essential for many recovery strategies, and 
directional flexibility often enables easier and more efficient 
operation of the strategies. Of course, the top.down left-to-right 
mode of operation is a characteristic of the network interpreter, 
not of the network formalism itself, and an attempt \[29\] has been 
made to operate an ATN in an "island" mode, i.e. bottom-up, 
center-out. This experiment was done in the context of a speech 
parser where the low-level recognition of many of the input words 
was uncertain, though the input as a whole was assumed to be 
grammatical. In that situation, there were clear advantages to 
starting with islands of relative lexicar certainty, and working out 
from them. Problems, however, arise during leftward expansion 
from an island when it is necessary to run the network backwards. 
The admissibility of ATN transitions can depend on tests which 
access the values of registers which would have been set earlier 
when traversing the network forwards, but which cannot have 
been set when traversing backwards. This leads at best to an 
increase in non-determinism, and at worse to blocking the 
traversal completely. 
4.2. Recovery strategies and pattern matching parsers 
A pattern matching approach to parsing provides a better 
framework to recover from some sentential level deviations than a 
network-based approach. In parttcular, the definition of what 
constitutes a pattern match can be relaxed to allow for missing or 
spurious constituents. For mis.~ing constituents, patterns which 
match some, but not all, of their components can be counted 
temporarily as complete matches, and spurious constituents can 
be ignored so long as they are embedded in a pattern whose other 
components do match. In these cases, the patterns taken as a 
whole provide a basis on which to perforrn the kind of "stepping 
back" discussed above as being vdal for flexible recovery. In 
addition, when pattern elements are defined semantically instead 
of lexically, as with Wilks' machine translation system\[26\], 
semantic constraints can easily be brought to bear on the 
recognition. However, dealing with out of order constituents is not 
so easy for a pattern-based approach since constituent order is 
built into a pattern in a rigid way, similarly to a network. It is 
possible to accept any permutation of elements of a pattern as a 
match, but this provides so much flex;bility that many spurious 
recognitions are likely to be obtained as well as the correct ones 
(see \[16\]). 
441 
An underlying problem here is that there is no natural way to 
make the distinctions about the relative importance or difference 
in role between one word and another. For instance, parsing 
many of our examples might have involved use of a pattern like: 
(~.determiner> ~disk-drive-attribute,~" ~disk-drive,~) 
which specifies a determiner, followed by zero or more attributes 
of a disk drive, followed by a phrase synonymous with "disk 
drive". So this pattern would recognize phrases like "a dual 
ported disk" or "the disk drive". Using the method of dealing with 
missing constituents mentioned above, "the" would constitute just 
as good a partial match for this pattern as "disk drive", a clearly 
undesirable result. The problem is that there is no way to tell the 
flexible matcher which components of the pattern are 
discriminating from the point of view of recognition and which are 
not. Another manifestation of the same problem is that different 
words and constituents may be easier or harder to recognize 
(e.g. prepositions are easier to recognize than the noun phrases 
they introduce), and thus may be more or less worthwhile to look 
for in an attempt to recover from a grammatical deviation. 
The underlying problem is the uniformity of the grammar 
representation and the method of applying it to the input. Any 
uniformly represented grammar, whether based on patterns or 
networks, will have trouble representing and using the kinds of 
distinctions just outlined, and thus is poorly equipped to deal with 
many grammatical deviations in an efficient and discriminating 
manner. See \[18\] for a fuller discussion of this point. 
4.3. Recovery strategies and case frame parsers 
Recursive case frame instantiation appears to provide a better 
framework for recovery from missing words than approaches 
based on either network traversal or pattern matchil~g. There are 
several reasons: 
• Case frame instantiation is inherently a highly interpretive 
process. Case frames provide a high-level set of syntactic 
and semantic expectations that can be applied to the input in 
a variety of ways. They also provide an overall framework 
that can be used to realize the notion of "stepping back" to 
obtain a broad view of a parser's expectations. 
o Case frame instantiation is a good vehicle for bringing 
semantic and pragmatic information to bear in order to help 
determine the appropriate parse in the absence of expected 
syntactic constituents. If a preposition is omitted (as 
commonly happens when dealing with cryptic input from 
hunt-and-peck typists), the resulting sentence is syntactically 
anomalous. However, semantic case constraints can be 
sufficiently strong to attach each noun phrase to the correct 
structure. Suppose, for instance, the following sentence is 
typed to an elec',ronic mail system interface: 
Send message John Smith 
The missing determiner presents few problems, but the 
missing preposition can be more serious. Do we mean to 
send a message "to John Smith", "about John Smith", "with 
John Smith", "for John Smith", "from John Smith", "in John 
Smith", "of John Smith", etc.? The domain semantics of the 
case frame rule out the latter three possibilities and others 
like them as nonsensical. However, pragmatic knowledge is 
required to select "to John Smith" as the preferred reading 
(possibly subject to user confirmation) -- the destination 
case of the verb is required for the command to be effective, 
whereas the other cases, if present, are optional. This 
knowledge of the underlying action must be brought to bear 
at parse time to disambiguate the cryptic command. In the 
XCALIBUR system case frame encoding \[10\], pragmatic 
knowledge of this kind is represented as oreference 
constraints (cf. \[26\]) on case fi!lers. This allows XCALIBUR to 
overcome problems created by the absence of expected case 
markers through the application of the appropriate domain 
knowledge. 
• The propagation of semantic knowledge through a case 
frame (via attached procedures such as those of KRL \[1\] or 
SRL \[30\]), can fiil in parser defaults and allow the internal 
completion of phrases such as "dual disks" to mean "dual 
ported disks". This process is also responsible for noticing 
when information is either missing or ambiguously 
determined, thereby initiating a focused clarificational 
dialogue \[15\]. 
• The representation of case frames is inherently non-uniform. 
Case fillers, case markers, and case headers are all 
represented separately, and thi$ distinction can be used by 
the parser interpretively mstantiating the case frame. For 
instance, if a case frame accounts for the non-spurious part 
of an input containing spurious constituents, a recovery 
strategy can skip over the unrecognizable words by scanning 
for case markers as opposed to case fillers which typically 
are much harder to find and parse. This ability to exploit 
non-uniformity goes a long way to overcoming the problems 
with uniform parsing methods outlined in the previous section 
on pattern matching. 
5. Dialogue Level Extragrammaticality 
The underlying causes of many extragrammaticalities detected 
at the sentential level are rooted in dialogue phenomena. For 
instance, ellipses and other fragmentary inputs are patently 
ungrammatical at the sentential level, but can be understood in 
the context of a dialogue. Viewed at this more global level, ellipsis 
is not ungrammatical. Nevertheless, the same computational 
mechanisms required to recover from lexioal and (especially) 
sentential problems are neces.~ary to detect ellipsis and parse the 
fragments correctly for incorporation into a larger structure. In 
general, many dialogue phenomena can be classified 
pragmatically as extragrammaticalities. 
In addition to addressing dialogue level extragrammaticalities, 
any robust parsing system must engage the user in dialogue for 
cooperative resolution of parsing problems too difficult for 
automatic recovery. Interaction with the user is also necessary for 
a cooperative parser to confirm any assumptions it makes in 
interpreting extragrammatical input and to resolve any ambiguities 
it cannot overcome on its own. We have referred several times in 
our discussions to the principle of tocused interaction, and stated 
that practical recovery dialogues should be focused as tightly as 
possible on the specific problem at hand. 
Because of space limitations, this paper does not discuss details 
the automated resolution of dialogue level extragrarnmaticalities 
or the use of dialogue to engage the user in cooperative 
resolution. The interested reader is referred to \[8\]. 
6. Concluding Remarks 
Any practical natural language interface must be capable of 
dealing with a wide range of extragrammatical input. This paper 
has proposed a partial taxonomy of extragrammatica!!ties that 
arise in spontaneously generated input to a restricted-domain 
natural language interface and has presented recovery strategies 
for handhng many of the categories. We also discussed how well 
three widely employed approaches to parsing -- network-based 
parsing, pattern matching, and case frame instantation -- could 
support the recovery strategies, and concluded that case frame 
instantiation provided the best basis The reader is referred to \[8\] 
442 
for a more complete presentation, including a more complete 
taxonomy and additional recovery strategies, particularly at the 
dialogue level. 
Based on the set of recovery strategies we have examined and 
the problems that arise in trying to integrate them with techniques 
for parsing grammatical input, we offer the following set of 
desiderata for a parsing process that has to deal with 
extragrammatical input: 
= The parsing process should be as interpretive as possible. 
We have seen several times the need for a parsing process to 
"stand back" and look at the broad picture of the set of 
expectations (or grammar) it is applying to the input when an 
ungrammaticality arises. The more interpretive a parser is, 
tbe better able it is to do this. A highly interpretive parser is 
also better able to apply its expectations to the input in more 
than one way, which may be crucial if the standard way does 
not work in the face of an ungrammaticality. 
• The parsing process should make it easy to apply semantic 
information. As we have seen, semantic information is often 
very important in resolving ungrammaticalities. 
= The parsing process should be able to take advantage of 
non-uniformity in language like that identified in Section 4.2. 
As we have seen, recovery can be much more efficient and 
reliable if a parser is able to make use of variations in ease of 
recognition or discriminating power between different 
constituents. Th~s kind of "opportunism" can be built into 
recovery strategies. 
= The parsing process should be capable of operating top. 
down as well as bottom-up. We have seen examples where 
both of these modes are essential. 
We believe that case frame mstantiation provides a better basis 
for parsing extragrammatical input than network-based parsing or 
pat!ern matching precisely because it satisfies these desiderata 
better than the other two approaches. We also believe that it is 
possible do even better than case frame instantiation by using a 
multi-strategy approach in which case frame instantiation is just 
one member (albeit a very important one) of a whole array of 
parsiag and recovery strategies. We argue this claim in detail in 
\[8,\] and support it by discussion of three experimental parsers that 
in varying degrees adopt the multi-strategy approach. 
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