A COMFUTATIONAL THEORY OF THE FUNCTION OF CLUE WORDS 
IN ARGUMENT UNDERSTANDING 
Robin Cohen 
Department of Computer Science 
University of Toronto 
'lDronto, CANADA MSS IA4 
A~TNACT 
This paper examines the use of clue words in 
argument dialogues. These are special words and 
phrases directly indicating the structure of the 
argument to the hearer. Two main conclusions are 
drawn: I) clue words can occur in conjunction with 
coherent transmissions, to reduce processing of the 
hearer 2) clue words must occur with more complex 
forms of transmission, to facilitate recognition of 
the argument structure. Interpretation rules to 
process clues are proposed. In addition, a 
relationship between use of clues and complexity of 
processing is suggested for the case of exceptional 
transmission strategies. 
! Overview 
In argt~nent dialogues, one often encounters words 
which serve to indicate overall structure - phrases 
that link individual propositions to form one 
coherent presentation. Other researchers in 
language understanding have acknowledged the 
existence of these "clue words". Birnbat~n 
\[Birnbaum 823 states that in order to recognize 
argument structures it would be useful to identify 
typical signals of each form. 
In \[Cohen 83\] we develop a computational model for 
argument analysis. The setting is a dialogue where 
the speaker tries to convince the hearer of a 
particular point of view; as a first step, the 
hearer tries to construct a representation for the 
structure of the arg~ent, indicating the 
underlying claim and evidence relations between 
propositions. Within this framework, a theory of 
linguistic clues is developed whlch categorizes the 
function of different phrases, presenting 
interpretation rules. 
What we have done is develop a model for argument 
analysis which is sufficiently well-defined in 
terms of algorithms, with measurable complexity, to 
allow convenient study of the effect of clue words 
on processing. Two important observations are 
made: (I) clue words cut processing of the hearer 
in recognizing coherent transmissions (2) clue 
words are used to allow the recognition of 
transmissions which would be incoherent (too 
complex to reconstruct) in the absence of clues. 
Considering arguments as goal-oriented dialogues, 
the use of clue words by the speaker can be 
construed as attempts to facilitate the heater's 
plan reconstruction process. Thus, there exist 
words and even entire statements with the sole 
function of indicating structure (vs. content) in 
the argument. The importance of structure to 
argument understanding is first of all a by-product 
of our imposed pragmatic approach to analysis. To 
understand the argument intended by the speaker, 
the hearer must determine, for each proposition 
uttered, both where it fits with respect to the 
dialogue so far and how, in particular, it relates 
to some prior statement. In addition, it is 
precisely the expected form of arguments which can 
be used to control the analysis (since content 
can't be stereotyped as in the case of stories). 
It is this importance of form which necessitates 
clue words and presents the research problem of 
specifiying their function precisely. 
II Background 
To understand the role of clue words in 
facilitating analysis, some detail on the overall 
argument understanding model is required. (For 
further reference, see \[Cohen 80\], \[Cohen 81\], 
\[Cohen 83\]). Each proposition of the argument is 
analyzed, in turn, with respect to the argument so 
far. A proposition is interpreted by determining 
the claim and evidence relations it shares with the 
rest of the argument's propositions. Leaving the 
verification of evidence to an oracle, the main 
analysis task is determining where a current 
proposition fits. 
To understand the examples introduced in this 
paper, it is useful to present the starting 
definition of evidence, as used in the model. A 
proposition P is evidence for a proposition O if 
there is some rule of inference such that P is 
premise to Q's conclusion. The rule most often 
observed is modus ponens, with missing major 
premise - i.e. P, Q are given and one must fill 
P --> Q to recognize the support intended from P to 
Q. More detail on the definition of evidence is 
presented in \[Cohen 83\]. 
Determining an interpretation for a proposition is 
restricted to a computationally reasonable task by 
characterizing possible coherent transmission 
251 
strategies on the part of the speaker and reducing 
analysis to a recognition of these forms. These 
algorithms are outlined in detail in \[Cohen 83\]. 
The basic restrictions yield e limited set of 
propositions to search. The representation is a 
tree of claim and evidence relations where evidence 
are sons to the father claim. Essentially, the 
last proposition eligible to relate to the current 
is tracked (called LAST). LAST and its ancestors 
in the tree are all eligible relatives and each is 
tested in turn, to set the interpretation of the 
current p~oposition. The analysis algorithm is 
termed "hybrid reception" because it is designed to 
recognize transmission strategies where each 
constituent sub-argument is presented either claim 
first or claim last. Complexity analysis of this 
algorithm indicates that it works in linear time 
(i.e. it takes a linear factor of the number of 
nodes of the tree to locate all propositions in tile 
representation). 
A sample tree and the processing required for the 
current proposition is illustrated below: 
initial" \[ final: 
2 4< I" /I/9~ 
/ ~ 5\6, ` ^/II~5\ 
7 z 3 "6x, 
i 
With the initial argument above, a new proposition 
(8) would be checked to be evidence for 7, 6, 5 and 
I in turn. If these tests fail, it is then 
attached as a son to the dummy root (expecting a 
father in upcoming propositions). The final tree 
above, for example, may result if the next 
proposition (9) is processed and succeeds as father 
to 8. Note that in processing 8 initially, 4, 3, 
and 2 were not eligible relatives. This is because 
an earlier brother to a subsequent proposition is 
closed off from consideration according to the 
specifications of the hybrid algorithm. See 
Appendix I for a detailed description of possible 
coherent transmission strategies and their 
"reception" algorithms. 
III Clues to reduce processing (Helpfulness) 
With coherent transmissions characterized, the 
role of clue words can be investigated more 
closely. Note first that the restrictions of the 
analysis algorithms are such that the proposition 
to which the current one relates is not always the 
immedimte prior proposition. In fact, sometimes 
the claim is located far back in the dialogue. 
Consider the following example: 
EXI: 1)The city is a mess 
2)The parks are a disaster 
3)The playground area is all run down 
4)The sandboxes arc dirty 
5)The swings are broken 
6)The highway system also needs 
revamping 
Here, the representation for the 
following tree: 
#2/'I~6 
argument is the 
The last proposition, b, is evidence for I, one of 
the claims higher up in the tree. Many arguments 
which re-address earlier claims assist the hearer 
by specifically including a clue of re-direction as 
in EX2 below. 
EX2: 1)The city is a mess 
2)The parks are a disaster 
3)The playground area is all run down 
4)The swings are broken 
5)The sandboxes are dirty 
6)Returning to city problems, the highway 
system needs revamping 
Here, the search up the right border of the tree 
(from 5, 3, 2 to I) for a possible claim to the 
current proposition b is cut short and the correct 
father (I) indicated directly. One can hypothesize 
a general reduction on processing complexity from 
linear to real-time, if clues are consistently used 
by the speaker to re-direct the hearer with chains 
that are sufficiently long. 
Connectives are another type of clue word, used 
extensively. Hobbs (\[Hobbs 76J) attempts a 
characterization with respect to his coherence 
relations for a couple of words. Reichman 
(\[Reichman 81\]) associates certain expressions with 
particular conversational moves, but there is no 
unified attempt at classification. We develop a 
taxonomy so that clues of the same semantic 
function are grouped to assign one interpretation 
rule for the dominated proposition within the claim 
and evidence framework. Consider the following 
example: 
EX3: 1)The city needs help 
2)All the roads are ruined 
3)The buildings are crumbling 
4)As a result, we are asking 
for federal support 
with the representation: 
2/I ~ 3 
The connective in 4, "as a result", suggests that 
some prior proposition connects to 4 and that this 
proposition acts as evidence for 4. 'lhe relation 
of the prior proposition is set out b.elow according 
the the interpretation rule for the category that 
"as a result" belongs to in the taxonomy. The 
particular evidence connection advocated here is of 
the form: "If our city needs help, then we will 
ask for federal aid". \[Note: Whether I is 
evidence for 4 is tested by trying a modus ponens 
major premise of the form: "(For all cities) if a 
252 
city needs help, then it can ask for federal aid", 
and then using "our city" as the specific case\]. 
The taxonomy (drawn from \[Quirk 72\]) is intended 
to cover the class of connectives and presents 
default interpretation rules. 
(P indicates prior proposition; S has the clue) 
CATEGORY RELATION:P to S EXAMPLE 
parallel brother in addition 
detail father in particular 
inference son as a result 
summary multiple sons in SL~n 
reformulation father and son in other words 
contrast father or brother conversely 
Note that the classification of connectives 
provides a reduction in processing for the hearer. 
For example, in EX3 with a casual connective, the 
analysis for the proposition 4 is restricted to a 
search for a son. In short, connective 
interpretation rules help specify the type of 
relation between propositions; re-direction clues 
help determine which prior proposition is related 
to the current one. All together, clue words 
function to reduce overall processing operations. 
See Appendix II for more examples of relations of 
the taxonomy. 
IV Clues to support complex transmissions (Necessity) 
C%ue words also exist in conjunction with 
transmissions which violate the constraints of the 
hybrid model of expected coherent structure. The 
claim is that clues provide a necessary reduction 
in complexity, to enable the hearer to recognize 
the intended structure. Consider the following 
examples: 
EX4: 1)The city is a mess 
2)The parks are run down 
3)The highways need revamping 
4)The buildings are crumbling 
5)The sandbox area is a mess 
EX5: 1)The city is a mess 
2)The parks are run down 
3)The highways need revamping 
4)The buildings are crumbling 
5)With regard to parks, 
the sandboxes are a mess 
6)As for the highways, the gravel is shot 
7)And as for the buildings, 
the bricks are rotting 
The initial tree for the argument is as follows: 
In EX4, the last proposition cannot be interpreted 
as desired; the probable intended father 
proposition (2) is not an eligible candidate to 
relate to the current proposition (5) according to 
.he hybrid specifications. In EX5, however, a 
parallel construction is specifically indicated 
through clue words, so that the connections can be 
recognized by the hearer and the appropriate 
representation constructed as below: 
11C--.. 5.2 6/3 7/4 
It now becomes important to provide a framework 
for accommodating "extended" transmission 
strategies in the model. First, the complexity of 
processing without clues is a good measure for 
determining whether a strategy should be considered 
exceptional. Then, to be acceptable in the model 
the proposed transmission must have some 
characterizable algorithm - i.e. still reflect a 
coherent plan of the speaker. Further, exceptional 
tranmsission strategies must be clearly marked by 
the speaker, using clues, in cases where the 
transmission can be assigned an alternate reading 
according to the basic processing strategy. The 
hearer should be expected to expend the minimum 
computational effort, so that the onus is on the 
speaker to make exceptional readings explicit. 
In brief, we propose developing a "clue 
interpetation module" for the analysis model, which 
would be called by the basic proposition analyzer 
to handle extended transmissions in the presence of 
clues. Then, complexity of processing should be 
used as s guide for determining the preferred 
analysis. 
To illustrate, consider another acceptable 
extended transmission strategy - mixed-mode 
sub-arguments, where evidence both precedes and 
follows a claim. 
EXd: l)The grass is rotting 
2)The roads are dusty 
3)The city is a mess 
4)In particular, the parks are a ruin 
Preferred rep: ..~..3~ Other possible rep: 
1 2 4 / \ 
I 2 
Here, it is preferable to keep I and 2 as evidence 
for 3, because this requires less computational 
effort than the re-attachment of sons which takes 
place to construct the other possible 
representation. In other words, computational 
effort is a good guide for the specification of 
processing strategies. 
Finally, it is worth noting that the specific clue 
word used may influence the processing for these 
extended transmissions. In EXd, if the last 
proposition (4) was introduced by the clue word "in 
addition", then the alternate tree would not be an 
eligible reading. This is because "in addition" 
forces 4 to find a brother among the earlier 
propositions, according to the interpretation rule 
for the "parallel" class of the taxonomy of 
253 
connectives. 
In sum, we propose particular extended 
transmission strategies for the model, including 
(i) parallel (ii) mixed-mode (iii) multiple 
relations. \[Note: More discussion of (iii) is in 
\[Cohen 33\]. We consider as an acceptable 
exceptional strategy the case where one proposition 
acts as evidence for an entire set of claims 
following it immediately in the stream. Other 
configurations of multiple relations seem to 
present additional processing problems\]. We demand 
clue words to facilitate the analysis and we begin 
to suggest how to accommodate uses of these 
exceptional cases in the overall analysis model. 
V Related Topics 
A. Nature of clues 
The exact specification of a clue is a topic for 
further research. Since it is hypothesized that 
clues are necessary to admit exceptional 
transmissions, what constitutes a clue is a key 
issue. Within Quirk's classification of 
connectives (\[Quirk 72\]) both special words and 
connecting phrases ("integrated markers") are 
possible. For example, one may say "in conclusion" 
or "I will conc\].ude by saying". 
Quirk also discusses several mechanisms for 
indicating connectives which need to be examined 
more closely as candidates for clue words. These 
comstructions are all "indirect" indications. 
a) lexical equivalence: This includes the case 
where synonyms are used to suggest a connection to 
a previous clause. For example: "The monkey 
learned to use a tractor. By age 9, he could work 
solo on the vehicle." In searching for evidence 
relations, the hearer may faciltate his analysis by 
recognizing this type of connective device. But it 
unclear that the construction should be considered 
an additional "clue". 
b) substitution, reference, comparison, ellipsis: 
Here, the "abbreviated" nature of the constructions 
may be significant enough to provide an extra 
signal to the hearer. For now, we do not consider 
these devices as clues, but examine the relations 
between the use of anaphors and clues in the next 
section. 
Even w!thin the classification of connectives, 
there is a question of level of explicitness of the 
clues. Consider the example: 
EX7: 1)The city is dangerous 
2a)I will now tel! you why this is so 
2b)The reason for the danger is... 
2c)The reason is... 
2d)The problem is ... 
2a) is an explicit indication of evidence; b) and 
c) have a phrase indicating a causal connection, 
but c) requires a kind of referent resolution as 
well; d) requires recognizing "the problem" as an 
indication of cause. The problem addressed in this 
example is similar to the one faced by Allen 
(\[Allen 79\]): handling a variety of surface forms 
which all convey the same intention. In our case, 
the "intention" is that one proposition act as 
evidence for another. 
Finally, there are different kinds of special 
phrases used to influence the credibilty of the 
hearer: I) attitudinal expressions reflecting the 
speaker's beliefs and 2) expressions of emphasis. 
Since our model focuses on the first step in 
processing of recognizing structural connections, 
these clues have not be examined more closely. 
However, examples of these expressions are listed 
in Appendix III, along with phrases indicating 
structure. 
B. Relation to reference resolution and focus 
There are some important similarities between our 
approach to reconstructing argument structure and 
the problem of representing focus for referent 
resolution addressed in \[Sidner 79\] and \[Grosz 77J. 
For both tasks, a particular kind of semantic 
re\]ation between parts of a dialogue must be found 
and verified. In both cases, a hierarchical 
representation is constructed to hold structural 
information and is searched in some restricted 
fashion. 
Orosz's hierarchical model of focus spaces, with 
visibility constraints imposed by the task domain, 
is maintained in a fashion similar to our tree 
model. Information on which of the focus spaces is 
"active" and which are "open" (possible to shift 
to) is kept; open spaces are determined by the 
active space and the visibilty constraints. 
Analysis for a problem such as resolving definite 
noun phrase referents can be limited by choosing 
only those items "in focus". 
In \[Sidner 79\] focus is introduced to determine 
eligible candidates for a co-specification. But 
the ultimate choice rests with verification by the 
hearer, using inferencing, that the focus element 
relates to the anaphor. This is parallel to our 
approach of narrowing the search for a 
proposition's intepretation, but requiring testing 
of possible relations in order to establish the 
desired link. To set the focus, Sidner suggests 
either: I) using special words to signal the 
hearer or 2) relying on shared knowledge to 
establish an unstated connection. This is 
analogous to our cases of processing with and 
without clues. 
In Sidner's theory there is also a clear 
distinction between returning to an element 
previously in focus (one from the focus stack) or 
choosing a completely "new topic" from prior 
elements (using the alternate focus list). We 
distinguish returning to some ancestor of the last 
proposition (a choice of eligible proposition) from 
the case of re-addressing a "closed" proposition. 
254 
In this latter case, we require a clue word to 
re-direct. What we have tried to do is clearly 
separate eligible relatives from exceptional cases 
and connect the required use of clues to the 
exceptional category. Grosz and Sidner both allow 
"focus shifts" and Sidner explicitly discusses uses 
of "special phrases", but we have tried to study 
the connections between clues and exceptions more 
closely. 
Finally, it is worth noting that the problem of 
reference resolution is similar to that of evidence 
determination, but still distinct. In the example 
below, constraints suggested by referent resolution 
theories should not be violated by our restricted 
processing suggestions: 
Exa: 1)The city is a mess 
2)The park is ruined 
3)The highway is run down 
4)Every 3 miles, you find a pothole in it 
In 4, "it" is resolved as referring to "the 
highway" in 3; this proposition is eligible and 
the closer connection is preferred. 
But clue interpretation is not equivalent to 
referent resolution. The clue "for example" may be 
expressed as "one example for this is" but could 
also be presented as "one example for this problem 
is". Since the search for a referent may differ 
according to the surface form (\[Sidner 79\]) there 
is no clear mapping from processing propositions 
with clues to those with referents. For our model, 
surface form may vary widely, but the search is 
restricted according to interpretation rules for a 
taxonomy - according to the semantics of the clue - 
and the solution is dictated by the structure of 
the argument so far. 
C. Necessity in the base case 
The main points raised in this paper are that 
clues can be used with a basic transmission 
strategy to cut processing and must be used in more 
complex transmissions. The question of whether 
certain basic transmissions still require clues is 
worth investigating further. In particular, it has 
been suggested (personal communication with 
psychologists) that deep stacks require clues to 
remind the hearer, due to "space" limitations. It 
may be productive to examine the computational 
properties of this situation more closely. 
Further, clues are often used to delineate 
sub-arguments when shifting topics. Again, some 
memory limitations for the hearer may be in effect 
here. 
VI Conclusion 
In conclusion, this paper outlines one crucial 
component of the computational model for argument 
analysis described in \[Cohen 83\]. It presents a 
first attempt at a solid framework for clue 
interpretation within argument understanding. The 
approach of studying goal-based dialogue and 
structure reconstruction also allows us to comment 
on the the function of clue words within analysis. 
The theory of clue interpretation gives insight 
into a known construction within sample dialogues; 
examining the computational properties provides a 
framework for design of the analysis model. It is 
important to note that there has been no effort to 
date to study the use of clue words extensively, 
distinguishing cases where they occur and 
suggesting when clues are necessary. The clue 
theory presented here also has possible 
implications for other application areas. For 
example, in resolving referents Sidner (\[Sidner 
79J) has suggested that clues will occur whenever 
the alternate focus list is consulted, beyond the 
focus stack default. Our claim is that the 
necessity for clues is closely tied to the 
complexity of processing and the reduction in 
processing operations afforded by the additional 
structural information provided by the clue words. 
REFERENCES 
\[Allen 79\] Allen, d.; "A Plan Based Approach to 
Speech Act Recognition"; University of Toronto 
Department of Computer Science Technical Report No. 
131 
\[Birnbatm 82\] Birnbaum, L.; "Argument Molecules: 
A Functional Representation of Argument Structure"; 
Proceedings of AAAI 82 
\[Cohen 80J Cohen, R.; "Understanding Arguments"; 
Proceedings of CSCSI 80 
\[Cohen 81\] Cohen, R.; "Investigation of 
Processing Strategies for the Structural Analysis 
of Arguments"; Proceedings of ACL 81 
LCohen 83J Cohen, R.; A Computational Model for 
the Analysis of Arguments; University of Toronto 
Department of Computer Science Ph.D. thesis 
(University of Toronto Computer Systems Research 
Group Technical Report No. 151) 
\[Grosz 77J Grosz, B.; "The Representation and, Use 
of Focus in Dialogue Understanding"; SRI Technical 
Note No. 151 
\[Hobbs 76\] Hobbs, J.; "A Computational Approach 
to Discourse Analysis"; Department of Computer 
Sciences, CUNY Research Report No. 76-2 
\[Quirk 72\] Quirk, R. et al. ; A Grammar of 
Contemporary English; Longmans Co., London 
\[Reichman 81\] Reichman, R.; "Plain Speaking: A 
Theory and Grammar of Spontaneous Discourse"; BBN 
Report No. 4681 
\[Sadock 77\] Sadock, J.; "Modus Brevis: The 
Truncated Argument"; in Papers from the 13th 
Regional Meeting, Chicago Linguistics Society 
255 
\[Sidner 79\] Sidner, C; "Towards a Computational 
Theory of Definite Anaphora Comprehension in 
English Discourse"; MIT AI Lab Report TR-537 
Appendix I: Coherent Transmission Strategies 
Coherent transmissions are illustrated and 
reception algorithms required to recognize these 
transmissions outlined. 1~is material is first 
introduced in \[Cohen 81\]. 
a)PRE-ORDER: state claim, then present evidence 
EXAI: 1)Jones would make a good president I 
2)He has lots of experience /\ 
3)He's been on the board for 10 years 2 4 
4)And he's honest I J 
5)He refused bribes while on the force 3 5 
In the above example, each claim consistently 
precedes its evidence in the stream of 
propositions. 
b)POST-ORDER: present evidence, then state claim 
EXA2: 1)Jones has been on the board 10 years 5 
2)He has lots of experience |\ 
3)And he's refused bribes 
4)So he's honest i i 
5)He would really make a good president I 3 
Here, the comparable example in post-order (where 
evidence precedes claim in the stream) is still 
coherent. 
The hearer can construct particular reception 
a\]gorithms to recognize either of the transmission 
strategies. To interpret a current proposition in 
the case of pro-order transmission, the hearer must 
simply look for a father: in fact, the test is 
performed only on the last proposition and its 
ancestors, up the right border of the tree. In 
post-order, the algorithm makes use of a stack to 
hold potential sons to the current proposition; 
the test is to be father to the top of the stack; 
if the test succeeds, all sons are popped and the 
resulting tree pushed onto the stack: if the test 
fails, the current proposition is added to the top 
of the stsck. 
c)HYBRID: any sub-argument may be in pre- or post- 
order 
EXA3: 1)Jones would make a good president I 
2)He has lots of experience /~ 
3)He's been on the board 10 years 2 5 
4)And he's refused bribes / 
5)So he's honest 3 4 
The above exgmple illustrates a coherent hybrid 
transmission. The hybrid reception algorithm is 
then a good approximation to a general processing 
strategy used by the speaker. Essentially, the 
algorithm combines techniques from pro- and post- 
order reception algorithms, where both a father and 
sons for a current proposition must be found. The 
search is still restricted, as certain propositions 
are closed off as eligible relatives to the current 
one, according to the specifications of the hybrid 
transmission. There is an additional problem, due 
to the fact that evidence is treated as a 
transitive relation. Sons are to be attached to 
their immediate father; so, it may be necessary to 
relocate sons that have been attached initially to 
a higher ancestor. This situation is illustrated 
below: 
Here, 4 any 5 would succeed as evidence for I 
(since they are evidence for 6 and 6 is evidence 
for I); they will initially attach to I and 
relocate as sons to 6 when 6 attaches as son to I. 
Here is an outline of the proposed hybrid reception 
algorithm. It makes uses of a dummy root node, for 
which all nodes are evidence. L is a pointer into 
the tree, representing the lowest node that can 
receive more evidence. For every node NEW in the 
input stream: 
forever do: 
if NEW evidence for L then 
if no sons of L are evidence for NEW then 
/* just test lastson for evidence*/ 
attach NEW below L 
set L to NEW 
exit forever loop 
else 
attach all sons of L which are 
evidence for L below NEW 
/* attach lastson; bump ptr. to lastson */ 
/* back I and keep testing for evidence */ 
attach NEW below L 
exit forever loop 
else set L to father (L) 
end forever loop 
APPENDIX II: Examples of Taxonomic Relations 
\[Cohen 81\] first suggests using common 
interpretation rules for connectives in one 
category of a taxonomy. Various examples presented 
in that paper are included here as additional 
background. In the discussion below, S refers to 
the proposition with the clue; P refers to the 
prior proposition which connects to S. 
1)Parallel: This category includes the most basic 
connectors like "in addition" as well as lists of 
clues (e.g. "First, secondly, thirdly..."). P 
must be brother to S. Finding a brother involves 
locating the common father when testing evidence 
relations. 
E~4: 1)The city is in serious trouble /I\ 
2)There are some fires going 2 4 
3)Three separate blazes have broken out ~3 
4)In addition, a tornado is passing through 
256 
The parallel category has additional rules for 
cases where lists of clues are present. Then, 
propositions with clues from the same list must 
relate. But note that it is not always a brother 
relation between these specific propositions. In 
fact, the brothers are the propositions which serve 
as claims in each sub-argument controlled by a list 
clue. 
EXA5: 1)The city is awful ~/I\4 
2)First, no one cleans the parks 
3)So the parks are ugly I \ 
4)Then the roads are a mess 2 5 
5)There's always garbage there 
Here, 2 and 4 contain the clues; 3 and ~ are 
brothers. 
2)Inference: There are clues like "therefore" 
which directly indicate inferences being drawn. 
The classification of "result" covers cause and 
effect relations which are of the form: if cause 
true then (most likely) effect true. Clues of this 
type are also included in the inference category. 
P will be son for S. 
EXA6: 1)The fire destroyed half the city 13 
2)People are homeless I 
3)As a result, streets are crowded I 
3)Detail: Included in this category are clues of 
example and particularization, where S lends 
partial support to P. Here, P will be father to S. 
EXAT: 1)Sharks are not likeable I~ 
2)They are unfriendly to humans 2% 
3)In particular, they eat people 3 
4)Summary: Ordinarily, summary suggests that a set 
of sons are to be found. S is father to a set of 
P's. 
EXA8: 1)The benches are broken /~ 
2)The trails are choppy I 2 3 
3)The trees are dying 
4)In sum, the park is a mess 
5)Reformulation: The taxonomy rule suggests 
looking for a prior proposition to be both father 
and son to the one with the clue. To represent 
this relation our tree model is inadequate. 
However, reformulations are often seen as 
additional evidence, adding detail and emphasis, 
and could then be recorded simply as sons to the 
prior statement. The example below suggests that 
interpretation: 
EXA9: 1)We need more money 
2)In other words, we are broke 
Note that additional discussion of the role of 
reformulation is included in \[Cohen 83\]. 
6)Contrast: Although the notion of contrast is 
complex, for now we interpret a proposition which 
offers contrast to some evidence for a claim as 
providing (counter) evidence for that claim, and 
hence S is a son of P; likewise, a proposition 
which contrasts another directly without evidence 
presented, is a (counter) claim, and hence S is a 
brother to P. 
EXAIO: 1)The city's a disaster 1~ 
2)The parks have uprooted trees 23 
3)But at least the playground's safe 
EXAlt: 1)The city is dangerous /~ 
2)The parks have muggers 4 3 
3)But the city has no pollution 2 
4)And there are great roads 
5)So, I think the city's great 
In EXAI0, the clue signals a s0n to higher claim; 
in EXA11, the clue connects two brother claims. 
APPENDIX III:Sample List of Clue Words 
This list is drawn from \[Quirk 72\]. Note that some 
words may belong to more than one category. 
I Coinciding with the connective taxonomy 
1:Parallel 
I first 17 on top of it all 
2 second..etc. 18 and what is more 
3 secondly..etc. 19 and 
4 next 20 neither...nor 
5 then 21 either...or 
6 finally 22 as well as 
7 last 23 rather than 
8 in the first place 24 as well 
9 for one thing 25 too 
10 for a start 26 likewise 
11 to begin with 27 similarly 
12 to conclude 28 equally 
13 furthermore 29 again 
14 moreover 30 also 
15 in addition 31 further 
16 above all 
\[Note that 24-31 are appositions; 20 - 23 operate 
between clauses in one sentence\]. 
2: Summary 
32 altogther 
33 overall 
34 therefore 
35 thus 
36 all in all 
37 in conclusion 
38 in sum 
39 to conclude 
40 to summarize 
41 I will sum by saying 
42 My conclusion is 
\[Note that 41 and 42 are whole phrases or 
"integrated markers"\]. 
3: Reformulation 
43 namely 45 that is to say 
44 in other words 46 alternately 
4: Detail 
47 for example 
48 for instance 
49 another instance is 
50 in particular 
257 
5: Inference 
51 that is 57 if so 
52 accordingly 58 if not 
53 consequently 59 That implies 
5a hence 60 l deduce from th,~b 
55 as a consequence 61 You can conclude from that 
56 as a result 
\[Note 57 and 58 operate betueen clauses within one 
sentence; 60 @nd 61 are whole phrases\]. 
6: Contrast 
62 otherwise 
63 conversely 
64 on the contrary 
65 in contrast 
66 by comparison 
67 however 
~8 nonetheless 
69 though 
70 yet 
71 in any case 
72 at any rate 
73 after all 
74 in spite of that 
75 meanwhile 
76 rather than 
77 I would rather say 
78 The alternative is 
\[Note 77 and 78 are whole phrases\]. 
II Attitudinal expressions 
These adverbs indicate a degree of belief of the 
speaker. 
primarily, principally, especially, chiefly, 
largely, mainly, mostly, notably, actually. 
certainly, clearly, definitely, indeed, obviously, 
plainly, really, surely, for certain, for sure. of 
course, frankly, honestly, literally, simply, kind 
of. sort of. more or less, mildly, moderately. 
partially, slightly, somewhat, in part. in some 
respects, to some extent, scarcely, hardly, barely. 
a bit. a little, in the least, in the slightest, 
almost, nearly, virtually, practically, 
approximately, briefly, broadly, roughly. 
admittedly, decidedly, definitely, doubtless. 
possibly, reportedly, amazingly, remarkably. 
naturally, fortunately, tragically, unfortunately, 
delightfully, annoyingly, thankfully, correctly. 
justly 
\[II Emphasis: indicate and defend ~ claim 
to be sure. it is true. there is little doubt, I 
admit, it cannot be denied, the truth is. in f~ct. 
in actual fact 
IV Transitions (re-directing structure) 
let us now turn to. spea\]'ing of. that reminds me 
Note that this appendix is not intended to list all 
possible clue words, but merely gives the reader an 
indication of the existing forms and possible 
categories. 
258 
