Robust parsing of severely corrupted spoken utterances 
Egidio P. Giachin Claudio Rullent 
CSELT - Centro Studi e Laboratori Telecomunicazioni 
Via Reiss Romoli 274, Torino, Italy - Ph. 439-11-21691 
Abstract 
This paper describes a technique for enabling a speech 
understanding system to deal with sentences for which 
some monosyllabic words are not recognized. Such words 
are supposed to act as mere syntactic markers within the 
system linguistic domain. This result is achieved by com- 
bining a modified caseframe approach to linguistic knowl- 
edge representation with a parsing strategy able to inte- 
grate expectations from the language model and predic- 
tions from words. Experimental results show that the 
proposed technique permits to greatly increase the quota 
of corrupted sentences correctly understandable without 
sensibly decreasing parsing efficiency. 
1 Introduction 
The problem addressed by this paper is how to make 
a speech understanding system deal wlth sentences for 
which some types of words are not recognized. 
The continuous speech understanding system under 
development at CSELT laboratories \[Fissore 88\] is part of 
a question-answerlng system allowing to extract informa- 
tion from a data base using voice messages with high syn- 
tactic freedom. The system is composed of a recognition 
stage \[Laface 87\] followed in cascade by an understand- 
ing stage. The recognition stage analyzes speech using 
acoustic-phonetic knowledge. Since utterances are spo- 
ken without pauses between words, it is not possible to 
uniquely locate words without using syntactic and seman- 
tic constraints. Thus the actual output of the recognition 
stage is a set:of word hypotheses, usually called lattice in 
the literature. A word hypothesis is characterized by its 
begin and end times, corresponding to the portion of the 
utterance in which it has been located, by a score repre- 
senting its belief degree, and by the lexeme itself. The 
understanding stage has the task of analyzing the word 
lattice using linguistic knowledge and producing a rep- 
resentation of the meaning of the most likely consistent 
word sequence. 
A two-stage approach to speech understanding of- 
fers several advantages and is the most widely followed 
in the current research. A serious difficulty, however, lles 
in the fact that often some short words that were actu- 
ally uttered are not detected by the recognltion level and 
hence they are missing from the lattice. To cope with this 
problem the understandlng stage must adopt a language 
representation and a parsing strategy which 1) whenever 
possible, do not rely on such words to understand a sen- 
tence, and 2) keep parsing efficiency comparable with the 
case in which no word is missing. This paper describes 
a technique for obtaining such results. The following is 
divided into four sections. The next one focuses on the 
various implications of word undetectlon on the linguis- 
tic processing. Then the linguistic knowledge bases of 
the understanding system and the parsing strategy are 
outlined (assuming that all words are present in the lat- 
tice). Next the technique for coping with missing words 
is introduced. Finally, experimental results are discussed, 
showing that the proposed technique permits to greatly 
increase the quota of corrupted sentences correctly under- 
standable without sensibly decreasing parsing efficiency. 
A discussion is also provided relating our results to other 
works addressing slmilar problems. 
2 A closer examination of the 
problem 
2.1 From the acoustical viewpoint 
The phenomenology of word undetectlon at the recogni- 
tion level is somewhat complex but mainly depends on 
word length. The dependency on length penalizes short 
words over long ones; i it is partly intrinsic to the signal- 
processing techniques used for recognition, and also hear° 
ily enhanced by coarticulatlon events. The consequence is 
that short words are frequently undetected or are given 
unreliable scores; then a standard parsing either would 
not work or would encounter heavy inefficiencies. 
There is also an additional problem for continuous 
1By 'short' word we mean a word described by one or two 
phonetic units. Phonetic units can be viewed approximately 
as phonemes \[Laface 87\]. 
This work hoJ been partially supported by the "EEC wlbhin the Esprit 
project ~6. 
196 
speech. Often short words are erroneously detected and 
assigned a good score. That happens frequently when 
their phonetic representation is also part of a longer word 
that was actually uttered. For this reason the efficiency of 
a traditional parser would be reduced due to the necessity 
of taking into consideration such nonexistent words. 
2.2 From the understanding view- 
point 
Short words span the widest range of lexical categories 
and have various degrees of 'significance' (take this term 
informally). Some cannot be eluded and, if they are miss- 
ingj it is necessary to understand the rest of the sen- 
fence and to initiate an additional interaction wlth the 
recognition level trying to figure out the most plausible 
words among a very limited set glven by the parser; if 
no accept~ble word is found, a dialogue with the user 
may be t~tarted, aimed at eliciting the essential infor- 
mation. Both are time-consuming operations; the lat- 
terp moreover, requires careful ergonomic considerations 
\[Kaplan 87,\]. However, there are words for which the sit- 
uation is 1Lot so drastic. This is the case of determiners, 
prepositions, and auxiliary verbs. 
The ~;reatment of words of these categories follow 
two main guidelines in the literature. In the former~ 
such words act mainly as syntactic markers for multi- 
word semantic constituents, without providing an intrin- 
sic semantic contribution. This philosophy includes case 
based \[Fillmore 68\] and conceptual-dependency based ap- 
proaches to natural language understanding \[Schank 75\]. 
In the latter guideline, such words play an independent 
role as semantic units and contribute compositionally 
wlth other words to the global meaning, with equal dig- 
nity \[Hinrlchs 86,Lesmo 85\]. Clearly, given the specific 
problem we are addressing, it is mandatory to follow the 
former guideline. Happily, this commitment is coherent 
with the preference granted to caseframe based parsing 
coming from different and independent reasons inherent 
in speech understanding (see \[Hayes 86\] for an excellent 
discussion). The peculiar caseframe based approach sum- 
marlzed in the next section provides in most cases the 
ability of understanding a sentence without relying on 
such word~. 
3 The standard parsing strat- 
egy 
3.1 Linguistic knowledge representa- 
tion 
Linguistic knowledge representation is based on the no- 
tion of cas~frame \[Fillmore 68\] and is described in detail 
in \[Poeslo 871. Caseframes offer a number of advantages 
h* speech parsing, hence their popularity in many recent 
speech understanding systems \[Hayes 86,Brietzmann 86\], 
but cause two main difficulties. 
First, the analysis cannot be driven by casemarkers, 
as is the case with written language, since often casemark- 
ere are just t!lose kinds of short words that are unreliably 
recognized or undetected at all. The standard approach 
is to assign to case headers the leading role, that is to 
instantlate caseframes using word hypotheses to fill their 
header slot and subsequently to try to expand the case 
slots. This strategy induces parsing to proceed in a top- 
down fashion, and works satisfactorily when headers are 
among the best-scored lexical hypotheses. However, it 
can be shown \[Gemello 87\] to cause severe problems if 
there is a bad-scored but correct header word, because 
the corresponding caseframe inetantiation will not be re- 
sumed until all of the caseffames having better-scored but 
false header words have been processed. The situation of 
headers with bad scores happens quite frequently, espe- 
cially when the uttered sentences suffer from strong local 
corruption due to coartlculatlon phenomena or environ- 
mental noise. Moreover, the standard strategy does not 
exploit the fact, dual to the one previously outlined, that 
some word hypotheses, though not being headers, have a 
good and reliable score. An integrated top-down/bottom- 
up strategy, able to exploit the predictive power of non- 
header words, is mandatory in such situations. 
A second difficulty is given by the integration of 
caseframes and syntax. This is due to two conflicting 
requirements. From one side, syntax should be defined 
and developed as a declaratlve knowledge base indepen- 
dently from caseframes, since this permits to exploit syn- 
tactic formalisms ~t the best and ins, ires ease of mainte- 
nance when the linguistic domain has to be expanded or 
changed. On the other hand, syntactic constraints should 
be used together with semantic ones during parsing, be- 
cause this reduces the size of the inferential activity. 
To overcome these problems~ caseframes and syntac- 
tic rules are pro-compiled into structures called Knowl- 
edge Sourcee (KSs). Each KS owns the syntactic and se- 
mantic competence necessary to perform a well-formed 
interpretation of a fragment of the input. Fig. 1 shows 
a simple caseframe, represented via Conceptual Graphs 
\[Sown 84\], and a simplified view of the resulting KS ob- 
tained by combining it with two rules of a Dependency 
Grammar \]Hays 64\]. The dependency rules are aug- 
mented wlth information about the functional role of the 
immediate constituents; this information id used by the 
offline compiler as a mapping between syntax and seman- 
tics necessary to automatically generate the KS. The KS 
accounts for sentences like "Da quale monte haste i1 Te- 
vere?" ("From which mount does the Tevere originate?"). 
The Composition part represents a way of grouping a 
phrase having a MOUNT type header satisfying the Ac- 
tivation Condition and a phrase having a RIVER type 
header. The Constraints part contains checks to be per- 
formed whenever the KS is operating. The Meaning part 
allows to generate the meaning representation starting 
197 
0F-24 
\[TO-HAVE-SOURCE\] --* (AGNT:Oompulsory) --+ RIVER 
--~ (LOC:Compulsory) --~ \[MOUNT\] 
DR-12.1 VERB(prop) = NOUN(interr-indlr-loe) <GOVERNOR> NOUN(subJ) 
;; Features and Agreements 
<GOVERNOR> (MOOD ind) (TENSE pres) (NUMBER _x) .... 
NOUN-I .... NOUN-2 (NUMBER _x) .... 
DR-12.2 VERB(prop) = NOUN(interr-indir-loc) <GOVERNOR> PROP-NOUN(~ubJ) 
DeIKS KS-24.12 
;;Composition 
TO-HAVE-SOURCE = MOUNT <HEADER> RIVER 
;;Constraints 
<HEADER>-MOUNT ((H-cat VERB) (S-eat NOUN) (H-feat MOOD ind TENSE pre .... ) ...) 
<HEADER>-RIVER ............... 
;;Header Activation Condition 
ACTION (TO-HAVE-SOURCE) 
;;Meaning 
(TO-HAVE-SOURCE I * agnt i los 0) 
Figure 1: A caseframe (expressed in CG notation), two dependency rules and a corresponding KS. 
rfype: TO-HAVE-SOURCE 
Header. "NASCK" 
Left: MOUNT Right: RIVER / 
Type: MOUNT 
Header: "MONTE" 
• JOE / Right: none 
# (to be solved) 
Type: RIVER 
Header: "TEV E FIE" 
Left: JOLLY Right: none -IL- 
\[missing\] 
Figure 2: An example of DI. 
from the meaning of the component phrases. 
3.2 Parsing 
Each of the phrase hypotheses generated by KSs dur- 
ing parsing relates to an utterance fragment and is called 
Deduction/natance (DI). Dis are an extension of the is- 
land concept in the HWIM system \[Woods 82\]. A DI is 
supported by word hypotheses and has a tree structure 
reflecting the compositional constraints of the KSs that 
built it. It has a score computed by combining the score 
of the word hypotheses supporting it. A simplified view 
of a DI is shown in Fig. 2. That DI refers to the sentence 
"Da quale monte nasce il 'revere?" ("From which mount 
does the Tevere originate?"); its root has been built by 
the KS of Fig. 1, and two more KSs were required to 
build the rest of it. The tree structure of the DI reflects 
the compositional structure of the KSs. The bottom-left 
part of the picture shows that there are two types (SPEC 
and JOLLY) that correspond to phrases that have still to 
be detected. Such 'empty' nodes are called goa/a. SPEG 
will account for the phrase "Quale" ("Which"); JOLLY 
represents the need of a preposition that might be missing 
from the lattice (this aspect is discussed later). 
Parsing is accomplished by selecting the best-scored 
DI or word hypothesis in the lattice and letting it to be 
accreted by all of the KSs that can do the job. Such 
opportunistic score-guided search results in top-down, 
'expectation-based' actions that are dynamically mixed 
with bottom-up, 'predictive' actions. The actions of KSs 
on Dis are described by operators. 
Top-down actions consist in starting from a DI having 
a goal, and: 
1. if it is a header slot, solve it with a word hypothesis 
(VERIFY operator); 
2. if it is a case-filler slot, 
• solve it with already existing complete Dis 
(MERGE), or 
• decompose it according to a KS knowledge 
contents (SUBGOALING). 
Bottom-up actions consist in creating a new DI start' 
ing either 
1. from a word hypothesis, which will occupy the 
header slot of the new DI (ACTIVATION), or 
2. from a complete DI, which will occupy a case-filler 
(PREDICTION). 
~198 , 
Such a strategy is opportunistic, since the element on 
which the KSs will work is selected according to its score, 
and the actions to be performed on it are determined 
solely by its characteristics. 
The activity of the operators is mainly concerned 
with the p:eopagation of constraints to the goal nodes of 
each newly-created DL Constraints are propagated from 
a father to a son or vice-versa according to the current 
parsing direction. They consist in: 
Time intervals, in the form of start and end ranges; 
Morphological information, used to check agree- 
men~s inside the DI; 
Fun(:tional information, used to verify the correct- 
ness of the grammatical relations that are being 
established within the DI; 
Semantic type information. This information is 
used when, unlike the case of Fig. 1, more than 
one caseframe are represented by a single KS (the 
offih~e compiler may decide to do this if the case- 
frames are similar and the consequent estimated re- 
duction of redundancy appears sufficiently great). 
In such a situation compliance with the single case- 
flames may have to be checked, hence the reason 
for this type of information. 
4 Dealing with missing short 
words 
As was pointed out, there are many different kinds of 
words thai. are short. In general, their semantic relevance 
depends on the linguistic representation and on the cho- 
sen domain. If the words are determiners, prepositions 
or auxiliary verbs, however, the integration of syntax and 
semantics outlined above makes them irrelevant in most 
cases, as very often it allows to infer them from the other 
words of the sentence. Such an inference may result not 
possible (mainly when prepositions are concerned), or the 
word may belong to other categories, such as connectives 
("and", "or") or proper nouns, which are short but whose 
semantic relevance is out of question; in these cases the 
system must react exactly as to the lack of a 'normal ~ 
word. 
Let us call 'jollies' the types of word for which only 
a functlonal role is acknowledged. Jollies are considered 
merely as ~yntactlc markers for constituents to which they 
do not offer a meaning contribution per se. The pursued 
goal is twofold: 
1. Par~3ing must be enabled to proceed without them 
in most cases; 
2. However~ whenever possible and useful, one wish 
to exploit their contribution in terms of time con- 
straint and score (remember that there are also 
~long' jollies, much more reliable than short ones). 
The general philosophy is 'ignore a jolly unless there 
is substantial reasons to consider it'. The proposed solu- 
tion is as follows: 
1. Jollies are represented as terminal slots in the com- 
positional part of a KS, like headers. There can be 
syntactic and even semantic constraints on them, 
but they do not enter into the rule describing the 
meaning representation. 
2. Since we assume that jollies have no semantic pre- 
dictive power, all of the operators are inhibited to 
operate on them. 
3. Another top-down operator, JVERIFY, is added to 
solve jolly slots, acting only when a DI has enough 
support from other 'significant' word hypotheses. 
Fig. 3 shows a KS deriving from the same caseffame of 
Fig. 1 but from a different dependency rule. Such a KS 
treats sentences like "Da quale monte si orlgina il Ten 
vere?" ("From which mount does the Tevere originate?"), 
in which the word "si" is a marker for verb reflexivity. 
The way JVERIFY operates depends on the re- 
sult of a predicate, JOLLY-TYPE, applied on the jolly 
slot. JOLLY-TYPE has three possible values: SttORT- 
OR-UNESSENTIAL, LONG-OR-ESSENTIAL, and UN- 
KNOWN that depend on various factors, including tlle 
lexical category assigned to the jolly slot, the temporal, 
morphologlc and semantic constraints imposed on that 
slot by other word hypotheses, and the availability of such 
data. if the returned value is LONG-OR-ESSENTIAL, 
then the jolly must be found in the lattice, and it~ loss 
causes parsing to react in a way exactly similar as to the 
loss of any other 'normal' word. Conversely, if the value 
is SHORT-OR-UNESSENTIAL, the jolly is ignored by 
placing a suitable temporal ~hole ~ in the slot pf the DI. 
The hole has relaxed temporal boundaries so as not to 
impose too strict a constraint o11 the position of words 
that can fill adjacent slots; thresholds are used for this 
purpose. Finally, if the value is UNKNOWN, an action 
like the previous one is done, followed by a limited search 
in the lattice, looking for words exceeding the maximum 
width of the 'hole'. Such a search is necessary because it 
insures that parsing does not fail when tim correct word 
is a jolly larger than the 'hole'. JVERIFY is submitted to 
the standard scheduling just as the other operators are. 
5 Experimental results 
The above ideas have been implemented in a parser called 
SYNAPSIS (from SYNtax-Aided Parser for Semantic In- 
terpretation of Speech). SYNAPSIS is an evolution of 
the parser included in the SUSY system for understand- 
ing speech and described in \[Poesio 87\]. SYNAPSIS has 
been implemented in Common Lisp and relies on about 
150 KSs, able to handle a 1011-word lexicon on a re- 
stricted semantic domain. An idea of the lingulstic cov- 
erage is given by the equivalent branching factor, which is 
199 
D1%-13.1 VEP~B(prop) = NOUN(interr-indir-loc) REFLEX <GOVERNOR,> NOUN(subj) 
;; Features and Agreements <GOVERNOR> (MOOD ind) (TENSE pres) (NUMBER .x) .... 
NOUN-1 .... 
I%EFLEX nil 
NOUN-2 (NUMBER ..x) .... 
DefKS KS-24.13 
;;Composition TO-HAVE-SOURCE= MOUNT <JOLLY> <HEADER> RIVER 
;;Meaning 
(TO-HAVE-SOURCE ! * agnt 1 ioc O) 
Figure 3: A KS with a jolly field. 
I I I" ,00, 1111 sentences present ,1,3 . 1 \[ 18 \[ 0 \[ O \] 
i i 0 b, lattices pre*snt 15 23 66 65 'l" l \] 11 \[ 
Table 1: Jolly word detection. 
miss|'ng 
jolly words 
per sentence 
n. of 
sentences 
successfully 
parsed 
average n. of 
generated Dis 
1 2 3 
40 18 4 
35 15 3 
318 440 563 
about 35. The system has been tested with 150 word lat- 
tices generated by processing as many sentences uttered 
in continuous speech with natural intonation in a normal 
office environment. The overall performance results in 
about 80% correct sentence understanding \[Fissore 88\]. 
The thresholds for JVERIFY have been experimen- 
tally determined to minimize the computational load, 
represented by the average number of Dis generated dur- 
ing each parsing. Tab. 1 shows the number of jolly words 
that have been skipped by the parser vs. the number 
of jollies actually missing in the corresponding lattices. 
The former figures are higher than the latter, indicating 
that many words, albeit present, have been discarded by 
JVERIFY because of their bad acoustical scores or their 
scarce contribution to contraint propagation. 
The most apparent advantage of the above technique 
is the increase in the number of sentences that can be an- 
alyzed without querying the user for lacking information. 
Tab. 2 displays the number of lattices, corresponding to 
the sentences containing at least one word of jolly type, 
in which some of such words are missing. It is seen that 
about 75~ of them have been successfully understood. 
This figure does not change substantially as the number 
of missing jollies per sentence increases, and hence indi- 
cates robustness. The computational load, given by the 
number of generated Dis, is somewhat affected by the 
number of missing jollies. However, this is mainly due to 
the fact that sentences with many jollies are also longer 
Table 2: Successful parsing 
and syntactically complex. The actual efficiency can be 
better estimated from Fig. 4, where the average number 
of generated Dis is plot as a function of the threshold on 
the width of the jolly temporal 'hole'. The figure displays 
also the amount of parsing failures related to jolly prob- 
lems (failures due to other reasons have been ignored for 
simplicity). The curve indicates that raising the thresh- 
old does not change much the number generated Dis (the 
relative oscillations of the values are small). This means 
that the relaxation of constraints during the application 
of JVERIFY is not a source of inefficiency. Moreover, 
there is a large range of values for which the parsing fail- 
ure remains low. 
The curve also shows that relaxing constraints may even 
speed up the parsing. This can be easily explained. When 
the threshold is low, no jolly is skipped, and failure oc- 
curs when jollies are missing from the lattice. When the 
threshold is raised, skipping begins to work: good-scored 
false jollies are no more a source of disturbance, and cor- 
rect but bad-scored jollies are skipped thus avoiding to 
delay the parsing; as a consequence the overall number 
of Dis decreases. Further enlarging the threshold reverts 
this tendency, since the too-much-relaxed constraints al- 
low the aggregation of words that would have been dis- 
carded with stricter constraints; failures occur when one 
of such aggregations makes up a complete parse scoring 
200 
Average nil ill|)el" of generated Dis 
(relative ut?its) k 
1 
0.95 -~ 
0.9 
0.85 -- 
Percentage of 
illc.Orl'C ct 
\[)Is mldcrstanding 
% N. I Y)~-_.( ' . ~{ % 
25% 
y:k -- 0 ...... 0 f -I- 0% 
_L k ...... t-. --t---- k ........ v . +---h 
-~ 
~1 10 15 20 211 3{i 35 Threshold on 
hole width 
{time li'ames) 
Figure 4: Performance vs. width threshold. 
better th~a the correc~ one. 
6 (Jonclnsmns and links with 
current research 
Experimer, ts show that the presence of jolly slots solvable 
as described above, beside permitting to successfully an- 
aly~e a much greater quota of word lattices, also speeds 
up parsing preventing it from being misled by false jollies. 
This well ::ompensatee for the growth of the inferential 
~tctivity dlte to the relaxed temporM constraints in the 
Dis contMning ~holes'. As a consequence it is possible 
~o use KS having chains of two or even three adjacent 
jolly slots without compromising excessively the global 
performai,,:es. This is a novel improvement over systems 
that, to our knowledge, only admit one single skippable 
word and use a more rigid linguistic knowledge repre- 
sentation \[Tomita 87\] or recognize any configuration of 
missing words but do not distinguish cases in which the 
information content of an absent word cart be ignored \[Goerz 83\]. 
An attracting feature of the present parsing tech- 
nique is th;~t the KS activities are modularized into a set 
of operators. Consequently, it remains open to 'local' im- 
provement~, on single operators as well as to overall heuris- 
tic adjustments on the score-guided control strategy. As 
aa exampi~, the response of the predicate JOLLY-TYPE 
of the oper~tor JVER3FY may be rendered more 'intelli- 
gent' by exploiting further information, such as estimates 
of the expected word length~ that has not been kept into 
consideration in the present implementation. 
A diff,!rent philosophy arising in very recent speech 
understanding research developments entrusts the prob- 
lem of solving troublesome portions of the utterance 
(including those were jollies were not found) to a 
deeper ,%eoustlcal analysis guided by linguistic expecta- 
tion \[Niedermair 87\]. Our approach is not in conflict, 
but rather, complementary to it. We believe that corn- 
bining the two approaches would lead to a research area 
that should turn very fruitful in producing robust speech 
parsing. 
The authors wi~h to ezpress their gratitude to their colleague, the 
late Dr. SuBs, for Id8 contribution to the develotnnent of the system. 

References 

\[Briet~.mann 86\] A.Brietzmann, U.Ehrlich, "The role of se- 
mantic processing ill an automatic speech understanding 
system", Pros COL1NG 86, Bonn. 

\[Fillmore 68\] C.J.Fillmore, "The case for case", in Bach~ Har- 
ris (eds.), Universals in Linguistic Theoryl Itolt, Rine- 
hart, and Winston, New York, 1968. 

\[Fissore 88\] L. Fissore, E. Giachin, P. Laface, G. Micca, R. 
Pieraccini, C. Rullent, "Experimental results on large- 
vocabulary speech recognition and understanding", Proc. 
ICASS"P 88, New York. 

\[Gemello87\] R. Gemello, E. Giachln~ C. Rullent, "A 
knowledge-based framework for effective probabilistic 
control strategies in signal understanding", Prvc. GWAI 
87, Springer Verlag ed. 

\[Goerv. 83\] G.Goerz, C.Beckstein, "How to parse gaps in spo- 
ken utterances", Proc. 1~t Conf. Europ. CnapL ACL. 

\[Hayes 86\] P.J. tIayes, A.G. Hauptmann, J.G. Carbonell, M. 
Tomita~ "Parsing spoken language: a semantic caseframe 
approach", Prec. COL1NG 86, Bonn. 

\[Itays 64\] D.G.Hays, "Dependency theory: a formalism and 
some observations", Memorandum RM4087 P.R., The 
Rand Corporation. 

\[Hinrichs 86\] E.W.ttinrichs, "A compositional semantics for 
directional modifiers", Proc. COLING 86, Bonn. 

\[Laface 87\] P.Laface, G.Micca, R.Pieraceini, "Experimental 
results on a large lexicon access task", Proc. ICASSP 87, 
Dallas. 

\[Lesmo 85\] L.Lesmo, P.Torasso, "Weighted interaction of syn- 
tax and semantics in natural language analysis", Pro¢. 
IJCAI85, Los Angeles. 

\[Kaplan 82\] S.J.Kaplan, "Cooperative responses from a 
portable natural language query system", Artificial Intel- 
ligence 19, 1982. 

\[Niedermair 87\] G.T.Niedermair, "Merging acoustics and lin- 
guistics in speech understanding", NATO ASI-Conference~ 
Bad Windsheim. 

\[Poesio 87\] M.Poesio, C.Rullent, "Modified caseframe parsing 
for speech understanding systems", Proc. IJCAI 87, Mi- 
lano. 

\[Schank 75\] R.Schank, Conceptual Information Processing, 
North-ttolIand, New York, 1975. 

\[Sows 84\] J.F.Sowa, Conceptual Structures, Addison Wesley, 
Reading (MA), 1984. 

\[Tomita 87\] M.Tomita, "An efficient augmented-context-free 
parsing algorithm", ComputationalLinguistic$, Vol. 13, n.1- 
2, Jan-June 1987. 

\[Woods82\] W.A.Woods, "Optimal search strategies for 
speech understanding control", Artificial Intelligence 18, 
1982. 
