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Abstract

The goal of understanding how discourse is more than a sequence of sentences has engaged researchers for many years. Researchers in the 1970's attempted to gain such understanding by identifying and classifying the phenomena involved in discourse. This was followed by attempts in the 1980s and early 1990s to explain discourse phenomena in terms of theories of abstract structure. Recent efforts to develop large-scale annotated discourse corpora, along with more lexically grounded theories of discourse are now beginning to reveal interesting patterns and show where and how early theories might be revised to better account for discourse data.
Behrens & Fabricius-Hansen (eds.) Structuring information in discourse:
the explicit/implicit dimension, Oslo Studies in Language 1(1), 2009. 171-190. (ISSN 1890-9639)
http://www.journals.uio.no/osla
 :
  
BONNIE WEBBBER
University of Edinburgh
RASHMI PRASAD
University of Pennsylvania

The goal of understanding how discourse is more than a sequence of sen-
tences has engaged researchers for many years. Researchers in the 1970’s at-
tempted to gain such understanding by identifying and classifying the phe-
nomena involved in discourse. This was followed by attempts in the 1980s
and early 1990s to explain discourse phenomena in terms of theories of ab-
stract structure. Recent efforts to develop large-scale annotated discourse
corpora, along with more lexically grounded theories of discourse are now
beginning to reveal interesting patterns and show where and how early the-
ories might be revised to better account for discourse data.
In the sciences, theory and data often compete for the hearts and minds of re-
searchers. In linguistics, this has been as true of research on discourse structure
as of research on syntax. Researchers’ changing engagement with data versus
theory in discourse structure – and the hope for more progress by engaging with
both of them – is the subject of this brief paper.
[1]  70:    
The 1970’s saw a focus on data, with Cohesion in English (Halliday and Hasan 1976)
an important milestone. This volume catalogued linguistic features in English
that impart cohesion to a text, by which Halliday and Hasan meant the network
of lexical, referential, and conjunctive relations which link together its different
parts. These relations contribute to creating a text from disparate sentences by
requiring words and expressions in one sentence be interpreted by reference to
words and expressions in the surrounding sentences and paragraphs.
[172]   
Of particular interest here are conjunctive elements, which Halliday and Hasan
took to signal how an upcoming sentence is related to what has been said be-
fore. Such conjunctive elements include both co-ordinating and sub-ordinating con-
junctions and what they call conjunctive adjuncts (eg, adverbs such as but,so,next,
accordingly,actually,instead,besides, etc.; and prepositional phrases such as as a
result,in addition,in spite of that,in that case, etc.).
More specifically, a conjunctive element is taken to convey a cohesive rela-
tion between its matrix sentence and that part of the surrounding discourse that
supports its effective decoding – resolving its reference, identifying its sense, or
recovering missing material needed for its interpretation. Three examples of con-
junctive elements can be found in this extract given from Meeting Wilfred Pickles,
by Frank Haley.
(1) a. Then we moved into the country, to a lovely little village called War-
ley.
b. It is about three miles from Halifax.
c. There are quite a few about.
d. There is a Warley in Worcester and one in Essex.
e. But the one not far out of Halifax had had a maypole, and a fountain.
f. By this time the maypole has gone, but the pub is still there called the
Maypole.
Halliday and Hasan labelled the adverb then in (1-a) a  
 (like next), which is a type of   ,
which is itself a type of  . It is decoded as conveying a sim-
ple sequential temporal relation to something in the preceding text (not provided
here). But in (1-e) was labelled a    
(like and), which is a type of a   , itself a
type of  .But is decoded here as conveying a simple con-
trastive adversative relation to (1-d). The final conjunctive element in this text,
by this time, Halliday and Hasan labelled a    
 (like until then), which is a type of   , which
is another type of  .By this time is decoded as conveying a
complex temporal relation to (1-a).
Although Halliday and Hasan provided a very elaborate taxonomy of conjunc-
tive elements in terms of the hierarchy of detailed labels for conjunctive relations
illustrated above, they did not embed it in any kind of theoretical framework that
would explain, for example, how meaning is projected systematically from a con-
junctive relation with a given label or what “surrounding text” a link can be made
to. Without such a theoretical framework, it was difficult to make use of their data
analysis in the systematic way required for computational applications.
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[2]  80  90:    
Providing a theoretic framework for what can be termed lexically grounded dis-
course relations was not, however, what researchers in the 1980s and 1990s were
concerned with. Rather, they aimed to provide a complete theoretical account
of a text in terms of abstract discourse relations. Such accounts included Rhetor-
ical Structure Theory (hereafter, RST) developed by Mann and Thompson (1988);
a theory (hereafter, GS) developed by Grosz and Sidner (1986) that posited three
separate but isomorphic discourse structures – an intentional structure, a lin-
guistic structure and an attentional structure; the Linguistic Discourse Model (here-
after, LDM) developed by Polanyi and her colleagues (Polanyi 1988; Polanyi and
van den Berg 1996); Relational Discourse Analysis (hereafter RDA) developed by
Moser and Moore (1996) as a way of reconciling RST and ; and Structured Dis-
course Representation Theory (hereafter ) developed by Asher and Lascarides
(2003) as an extension to DRT (Kamp and Reyle 1993) to account for how discourse
relations arise from what the authors call commonsense entailment (Lascarides and
Asher 1993). The abstract discourse relations used in these theories included both
semantic relations between the facts, beliefs, situations, eventualities, etc. de-
scribed in a text (more often called informational relations) and pragmatic relations
between what a speaker is trying to accomplish with one part of a text with re-
spect to another (more often called intentional relations). Together, these are often
simply called discourse relations.
Unlike the lexically-grounded discourse relations of Halliday and Hasan (1976),
theories of abstract discourse relations see comprehensive structure (based on
discourse relations) underlying a text, just as theories of syntax see a comprehen-
sive syntactic structure underlying a sentence. In particular, theories of abstract
discourse relations assume, as in formal grammar, a set of terminal elements
(called either elementary or basic discourse units). A discourse relation holding
between adjacent elements joins them recursively into a larger unit, with a text
being recursively analysable down to its terminal elements. Such an analysis cov-
ers the entire text, just as in syntax a single parse tree or dependency analysis
covers an entire sentence. Also as in syntax, the analysis is essentially a tree
structure since, for the most part, it is also assumed that no discourse element
is part of more than one larger element. Where these theories differ is in (1) the
specific types of relations they take to hold between units; (2) the amount of at-
tention they give to, eg, how a hearer establishes the relation that holds between
two units; (3) whether they take there to be separate but related informational and
intentional discourse structures (GS, ) or a single structure (RST, ,);
and (4) how to provide a compositional syntactic-semantic interface that would
systematically interpret a discourse unit in terms of the interpretations assigned
to its component parts.
These theories were not without practical application, and came to under-
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pin work in Natural Language Generation (Marcu 1996;Mellish et al. 1998;Moore
1995) and document summarization (Bosma 2004;Marcu 1998,2000). However,
these theories were based on very little data, and problems began to be noticed
early on. For example, Moore and Pollack (1992) noticed that the same piece of
text could simultaneously be given different  analyses, each with a different
structure. These were not alternative analyses that could be disambiguated: all
of them seemed simultaneously appropriate. But if this was the case, what was
the consequence for associating a text with a single discourse structure (or even
structurally isomorphic intentional and informational structures)?
Elsewhere, Scott and de Souza (1990) and later Carlson et al. (2003) pointed
out that the meaning conveyed by a sequence of sentences or a complex sentence
could also be conveyed by a single clause. But if this was the case, did it make
sense to posit an independent existence for elementary discourse units?
And questions about discourse having an underlying recursive tree-like struc-
ture were raised by Wiebe (1993) based on examples like
(2) a. The car was finally coming toward him.
b. He [Chee]finished his diagnostic tests,
c. feeling relief.
d. But then the car started to turn right.
The problem she noted was that the discourse connectives but and then appear to
link clause (2-d) to two different things: then to clause (2-b) in a  rela-
tion – i.e., the car starting to turn right being the next relevant event after Chee’s
finishing his tests – and but to a grouping of clauses (2-a) and (2-c) – i.e., reporting
a contrast between, on the one hand, Chee’s attitude towards the car coming to-
wards him and his feeling of relief and, on the other hand, his seeing the car turn-
ing right. But a structure with one subtree over the non-adjacent units (2-b) and
(2-d) and another over the units (2-a),(2-c) and (2-d) again is not itself a tree. Did
such examples really raise a problem for postulating an underlying comprehen-
sive tree-like structure for discourse, and if they did, what kind of comprehensive
structure, if any, did discourse have?
[3]   :   
While the problems noted above weren’t enough to refute theories of abstract
discourse relations, two distinct currents gaining momentum in sentence-level
syntax through the 1990s have led discourse research to re-focus on data again
in the new century. These were the emergence of (1) part-of-speech and syn-
tactically annotated corpora such as the Penn TreeBank (Marcus et al. 1993); and
(2) lexicalized grammars, in which syntactic contexts were associated with words
directly rather than indirectly through phrase structure rules (eg, syntactic con-
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texts in the form of tree fragments in Lexicalized Tree-Adjoining Grammar (LTAG)
(Schabes 1990;XTAG-Group 2001) and in the form of complex categories in Com-
binatory Categorial Grammar (CCG) (Steedman 1996,2000)).
In Section 3.1, we will make some general remarks about annotated corpora,
followed by a brief description in Section 3.2 of a lexicalized grammar for dis-
course, and then finally, in Section 3.3, a bit about a particular annotated dis-
course corpus – the Penn Discourse TreeBank – that was stimulated by this work
to return to a focus on lexically-grounded discourse relations. We will close with
some predictions about the future.
[3.1] Annotated Discourse Corpora
While the automatically generated, manually corrected part-of-speech and syn-
tactic annotation that makes up the Penn TreeBank (PTB) Wall Street Journal Cor-
pus was developed as a community-accepted “gold standard” on which parsers
and parsing techniques could be evaluated (Marcus et al. 1993), the PTB soon be-
came a basis for inducing parsers using statistical and machine learning tech-
niques that were much more successful in wide-coverage parsing than any ones
previously developed. Similar techniques based on appropriately annotated cor-
pora enabled the development of other language technology, including part-of-
speech taggers, reference resolution procedures, semantic role labellers, etc., which
are beginning to be used to improve performance of applications in information
retrieval, automated question answering, statistical machine translation, etc.
Eventually, researchers turned to consider whether annotated discourse cor-
pora can yield similar benefits by supporting the development of technology that
required sensitivity to discourse structure, such as in extractive summarization,
where one identifies and includes in a summary of one or more source texts, only
their most important sentences, along with, perhaps, other sentences needed to
make sense of them. This was one of the motivations behind the creation of the
 Corpus (Carlson et al. 2003), comprising 385 documents from the Penn Tree-
Bank corpus that have been manually segmented into elementary discourse units,
linked into a hierarchy of larger and larger units, and annotated with  relations
taken to hold between linked units. It was also the acknowledged reason for de-
veloping an -annotated corpus (Polanyi et al. 2004). Another corpus was an-
notated according to  (Moser and Moore 1996), with an aim of improving the
quality of Natural Language Generation (NLG) – in particular, to identify which of
several syntactic variants is most natural in a given context – information that can
then be incorporated into the sentence planning phase of NLG (Di Eugenio et al.
1997). (Prasad et al. (2005) discusses how the Penn Discourse TreeBank, to be dis-
cussed in Section 3.3, can also be used for this purpose.)
While the promise of benefits for Language Technology have helped attract
funding for annotated discourse corpora, such corpora serve other objectives as
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[176]   
well. For example, researchers want to use them to test hypotheses about the
effect (or co-dependency) of discourse structure on other aspects of language
such as argumentation (Stede 2004;Stede et al. 2007) or reference resolution (cf.
work done at the University of Texas on a corpus annotated according to 
(Stede et al. 2007)).
But a fundamental reason for developing annotated discourse corpora is to
enable us to advance towards a theoretically well-founded understanding of dis-
course relations – ie, of how they arise from text, or of constraints on their possi-
ble arguments, or of how the resulting structures pattern – that is well-grounded
in empirical data. This was a main goal behind the development of the Discourse
GraphBank (Wolf and Gibson 2005).1And already there has been a real advance in
discourse theory based on empirical data (Stede 2008): Problems with annotating
 relations in the Potsdam Commentary Corpus (Stede 2004;Stede et al. 2007)
led Stede to examine in detail the notion of nuclearity that has been fundamental
to  as a theory but is problematic in practice. The result is an argument for
nuclearity as primitive only for intentional discourse relations. For informational re-
lations, nuclearity is best discarded in favor of independently motivated notions of
discourse salience or prominence (associated with entities) and discourse topic. Before
turning to a brief description of the Penn Discourse TreeBank (Miltsakaki et al.
2004;Prasad et al. 2004;Webber 2005) and some things we have already learned
from the process of creating it, we will briefly describe the lexicalized approach
to discourse that led to the creation of this lexically-grounded corpus.
[3.2] Discourse Lexicalized Tree-Adjoining Grammar (D-LTAG)
D-LTAG is a lexicalized approach to discourse relations, which aims to provide an
account of how lexical elements (including phrases) anchor discourse relations
and how other parts of the text provide arguments for those relations (Webber et al.
2003;Webber 2004). D-LTAG arose from a belief that language has only a limited
number of ways to convey relations between things. For example, within a clause,
a verb or preposition can convey a relation holding between its arguments
(eg, the cat ate the cheese; the cat in the hat), as can some nouns and adjec-
tives (eg, the love of man for his family; an easy mountain to climb);
adjacency of two or more elements can convey implicit relations holding
between them, as in soup pot,soup pot cover,aluminum soup pot cover adjust-
ment screw, etc;
an anaphoric expression conveys a relation between all or part of its deno-
tation and some element of the surrounding discourse – an  rela-
[1] (Webber 2006) argues against the strong claims (Wolf and Gibson 2005) make about discourse structure
based on this annotation, and many of them have subsequently been withdrawn (Kraemer and Gibson
2007).
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tion in the case of coreference, other relations in the case of comparative
anaphora such as the smaller boys;
intonation and information structure can convey relations of  be-
tween discourse elements and/or their different roles with respect to infor-
mation structure (Steedman 2007).
If one assumes that many of the same means are operative outside the clause as
within it, then it makes sense to adopt a similar approach to discourse analysis
as to syntactic analysis. Since lexicalized grammars seemed to provide a clearer,
more direct handle on relations and their arguments at the clause-level, D-LTAG
adopted a lexicalized approach to discourse based on lexicalized Tree Adjoining
Grammar (Schabes 1990). A lexicalized TAG (LTAG) differs from a basic TAG in tak-
ing each lexical entry to be associated with the set of elementary tree structures
that specify its local syntactic configurations. These structures can be combined
via either substitution or adjoining, to produce a complete sentential analysis.
The elementary trees of D-LTAG are anchored by discourse connectives whose
substitution sites correspond to their arguments. These can be filled by anything
interpretable as an abstract object (ie, as a proposition, fact, eventuality, situation,
etc.). Elements so interpretable include discourse segments, sentences, clauses,
nominalisations and demonstrative pronouns. Adjacency in D-LTAG is handled
by an elementary tree anchored by an empty connective.
As with a sentence-level LTAG, there are two types of elementary trees in D-
LTAG: Initial trees, anchored by structural connectives such as subordinating con-
junctions and subordinators (eg, in order to,so that, etc.) illustrated by the trees
labeled α:so and α:because_mid in Figure 1(a), and auxiliary trees, anchored by a
coordinating conjunction or an empty connective or by a discourse adverbial, illus-
trated by the trees labeled β:but and β:then in Figure 1(a).
Both the initial trees of structural connectives and the auxiliary trees of co-
ordinating conjunctions and the empty connective reflect the fact that both their
arguments are provided through structure – in the case of initial trees, the two
substitution arguments labelled with in α:so and α:because_mid, and in the case
of auxiliary trees, the one substitution argument labelled with and the adjunc-
tion argument labelled with *, as in β:but. In contrast, a discourse adverbial is
anaphoric – with the discourse relation, part of its semantics, but with one of its
arguments coming from the discourse context by a process of anaphor resolution.
Its other argument is provided structurally, in the form of its matrix clause or
sentence. That discourse adverbials such as instead,afterwards,as a result, etc. are
anaphoric, differing from structural connectives in getting their second argument
from the discourse context, is argued on theoretical grounds in (Webber et al.
2003) and on empirical grounds in (Creswell et al. 2004). It also echoes in part
the claim of Halliday and Hasan (1976), noted in Section 1, that all conjunctive el-
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[178]   
*
cancel discover
love order cancel
love order
α:so
so
β:then
*
then
because
α:because_mid
β: but
T4
T3
so
T1 T2 T3
T4
but
because
then
but
T1 T2
(a) Derived Tree for Example (3).
α:so
β: but
α:because_mid
β:then
3
10
3
13
0
T1 T2
T3 T4
α:so
β: but
α:because_mid
β:then
3
10
3
13
0
T1 T2
T3 T4
(b) Derivation Tree for Example (3)
 1: Tree analyses of Example (3)
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ements were interpreted in this way. Justification of the anaphoric character of
discourse adverbials is given in (Forbes 2003) and (Forbes-Riley et al. 2006).
Discourse relations arising from both structural and anaphoric connectives
can be seen in the D-LTAG analysis of Example (3) below.
(3) John loves Barolo.
S he ordered three cases of the ’97.
B he had to cancel the order
 he  discovered he was broke.
Figure 1(a) illustrates both the starting point of the analysis — a set of elementary
trees for the connectives (so, but, because, then) and a set of leaves (T1-T4) for the
four clauses in Example (3) without the connectives — and its ending point, the
derived tree that results from substituting at the nodes in α:because_mid,α:so and
β:but marked and adjoining the trees β:but and β:then at their nodes marked .
These operations of substitution and adjoining are shown as solid and dashed lines
respectively in Figure 1(b), in what is called a derivation tree. The numbers on the
arcs of the derivation tree refer to the node of the tree that an operation has been
performed on. For example, the label 1on the solid line from α:so to T1 means
that T1 has substituted at the leftmost node of the tree α:so. The label 3refers to
the node that is third from the left. The label 0on the dashed line from α:so to
β:but means that β:but has adjoined at the root of α:so.
The structural arguments of a connective can come about through either sub-
stitution or adjoining. The derived tree in Figure 1(a) shows the two structural
arguments of so,but and because as their left and right sisters. The derivation tree
in Figure 1(a) shows both arguments to so and because coming from substitution,
with one structural argument to but coming from substitution and the other com-
ing through adjoining. Finally, then only has one structural argument, shown in
Figure 1(a) as its right sister. Figure 1(b) shows it coming from adjoining. The dot-
ted line in Figure 1(a) shows then linked anaphorically to the clause that gives rise
to its first argument. More detail on both the representation of connectives and
D-LTAG derivations is given in (Webber et al. 2003). A preliminary parser produc-
ing such derivations is described in (Forbes et al. 2003) and (Webber 2004).
Compositional interpretation of the derivation tree produces the discourse
relation intepretations associated with because,so and but, while anaphor resolu-
tion produces the second argument to the discourse relation interpretation asso-
ciated with then (ie, the ordering event), just as it would if then were paraphrased
as soon after that, with the pronoun that resolved anaphorically. Details on this
syntactic-semantic interface are given in (Forbes-Riley et al. 2006).
Although D-LTAG produces only analyses in the form of trees, Webber et al.
(2003) recognized that occasionally the same discourse unit participates in one
relation with its left-adjacent material and another distinct relation with its right-
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[180]   
adjacent material, as in
(4) A   the tremor passed,many people spontaneously arose and cheered,
  it had been a novel kind of pre-game show.2
(5) W Ms. Evans took her job,several important divisions that had reported
to her predecessor weren’t included   she didn’t wish to be a
full administrator.
In Example (4), the main clause “many people spontaneously arose and cheered”
serves as ARG1 to both the subordinating conjunction    on its left and
the subordinating conjunction   on its right. Example (5) shows a sim-
ilar pattern. Such examples, however, would require relaxing substitution con-
straints in D-LTAG that the same tree only substitutes into a single site. This has
not yet been done. It should be clear that D-LTAG is only a conservative extension
of the theories mentioned in Section 2 in that it shares their assumptions that
a text can be divided into discourse units corresponding to clauses, with a dis-
course analysis covering those units – hence the work on developing a discourse
parser for D-LTAG (Forbes et al. 2003;Webber 2004). The main way in which D-
LTAG diverges from these other theories is in anchoring discourse relations in, on
the one hand, structural connectives and adjacency, and on the other, anaphoric
connectives. The latter provide additional relations between material that is not
necessarily adjacent, but not in a way that changes the complexity of discourse
structure: D-LTAG analyses are still trees.
[3.3] The Penn Discourse TreeBank
The Penn Discourse TreeBank (PDTB) annotates discourse relations in 2304 articles
of the Wall Street Journal corpus (Marcus et al. 1993) in terms of discourse connec-
tives, the minimal text spans that give rise to their arguments, and the attribu-
tion of both the connectives and their arguments (Dinesh et al. 2005;Prasad et al.
2007). For example, in Example (6)
(6) Factory orders and construction outlays were largely flat in December 
purchasing agents said manufacturing shrank further in October.
both ARG1 and the connective while are attributed to the writer, while ARG2 is
attributed to someone else via the attributive phrase “purchasing agents said”,
which is not included in that argument.
Primarily two types of connectives have been annotated in the PDTB: explicit
connectives and implicit connectives, the latter being inserted between adjacent
[2] Both these examples come from the Penn Discourse TreeBank (Section 3.3) and reflect the actual an-
notation of these connectives. In all PDTB examples, ARG1 is in italics and ARG2 in boldface, and the
connective is underlined.
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C A N T
S-medial so 147 2 149
S-medial but 213 0 213
Total 360 2 362
S-initial So 89 22 111
S-initial But 347 63 410
Total 436 85 521
 2: S-medial vs. S-initial Connectives
paragraph-internal sentences not related by an explicit connective. As in D-LTAG
(Section 3.3), explicit connectives include coordinating conjunctions, subordinat-
ing conjunctions and discourse adverbials. The argument associated syntactically
with the discourse connective is conventionally referred to as ARG2 (eg, the sub-
ordinate clause of a subordinating conjunction) and the other argument as ARG1.
Because annotators were asked to annotate only the minimal span associated with
an argument, they were also allowed (but not required) to indicate spans adjacent
to ARG1 and ARG2 that were relevant to them but still supplementary, using the
tags SUP1 and SUP2.
A preliminary version of the PDTB containing the annotation of 18505 explicit
connectives was released in April 2006 (PDTB-Group 2006), and received over 120
downloads. The completed PDTB (Version 2.0) was released by the Linguistic Data
Consortium (LDC) in February 2008, and includes annotation of all implicit con-
nectives as well, along with a hierarchical semantic annotation of both explicit
and implicit connectives (Miltsakaki et al. 2008). More information on the PDTB
can be found at its homepage (http://www.seas.upenn.edu/~pdtb).
Although annotation has sometimes been a challenge, it has nevertheless be-
gun to yield some useful observations. One such observation is that the position
of ARG1 can differ significantly, depending on whether its associated connective
occurs sentence-medially (S-medial) or sentence-initially (S-initial). Figure 2con-
trasts the instances of S-medial and S-initial so and but in the corpus3with respect
to whether their ARG1 spans the immediately preceding clause(s) or sentence(s)
(Adjacent) or not (Non-adjacent). This difference in patterning between medial
instances of but and so and sentence-initial instances, is statistically significant
(<.0001).
The difference will certainly be relevant to those researchers interested in
developing the technology to automatically recognize the arguments to discourse
[3] With But, figures are based on the first 213 of 1188 S-medial instances in the corpus and the first 410 of
2124 S-initial instances.
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[182]   
connectives (cf. Wellner and Pustejovsky 2007). But it is not just the distance of
ARG1 from its connective: it is also a difference in function. As with S-initial but
and so,A1 of S-initial discourse adverbials like instead can also be found at a
distance – eg,
(7) On a level site you can provide a cross pitch to the entire slab by raising
one side of the form, but for a 20-foot-wide drive this results in an awkward
5-inch slant across the drive’s width. I,make the drive higher at
the center.
(Reader’s Digest New Complete Do-it-yourself Manual, p. 154)
(8) If government or private watchdogs insist, however, on introducing greater fric-
tion between the markets (limits on price moves, two-tiered execution, higher mar-
gin requirements, taxation, etc.), the end loser will be the markets themselves.
I,we ought to be inviting more liquidity with cheaper ways to
trade and transfer capital among all participants.
But of the 48 occurrences of S-initial Instead in the corpus, ARG1 spans text other
than the main clause in 26 (54.2%), independent of its position with respect to
the connective. In Example (8), ARG1 spans the subordinate clause, and in Exam-
ple (9), the gerund complement of an appositive NP:
(9) The tension was evident on Wednesday evening during Mr. Nixon’s final
banquet toast, normally an opportunity for reciting platitudes about eternal
friendship.I,Mr. Nixon reminded his host, Chinese President
Yang Shangkun, that Americans haven’t forgiven China’s leaders for
the military assault of June 3-4 that killed hundreds, and perhaps
thousands, of demonstrators.
This suggests that ARG1 of a discourse adverbial like instead comprises a span with
a specific semantic character — here, one that can be interpreted as something
for which an alternative exists (Webber 2004). The span can be anywhere in the
sentence and serve any role. In contrast, ARG1 of S-initial So or But comprises
a span at the same level of discourse embedding as ARG2, with any intervening
material appearing to serve a supporting role to the span identified as ARG1, as
in
(10) The 40-year-old Mr. Murakami is a publishing sensation in Japan. A more re-
cent novel, “Norwegian Wood” (every Japanese under 40 seems to be flu-
ent in Beatles lyrics), has sold more than four million copies since Kodan-
sha published it in 1987. B he is just one of several youthful writers
– Tokyo’s brat pack – who are dominating the best-seller charts in
Japan.
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(11) It is difficult, if not impossible, for anyone who has not pored over the thousands
of pages of court pleadings and transcripts to have a worthwhile opinion on the
underlying merits of the controversy. Certainly I do not. S we must look
elsewhere for an explanation of the unusual power this case has ex-
erted over the minds of many, not just in Washington but elsewhere
in the country and even the world.
Additional (albeit incomplete) evidence for this role of the intervening text comes
from the frequency with which some or all of the intervening material has been
annotated SUP1 in the PDTB, even though the annotators were not required to be
systematic or rigorous in their use of this label.
This pragmatic/intentional sense of a supporting role (or a difference in fo-
cality or discourse salience) appears to be what Mann and Thompson (1988) had in
mind with presentational rhetorical relations in which one argument (the satellite)
supported the other argument (the nucleus) – and what Grosz and Sidner (1986)
had in mind when they posited a dominance relation in which one discourse seg-
ment supported the discourse purpose of another. It also appears related to what
Blühdorn (2007) and Ramm and Fabricius-Hansen (2005) refer to as subordination
in discourse. It has not been directly annotated in the PDTB, which has focussed
on the arguments to explicit and implicit connectives. On the other hand, we
should be able to gather additional evidence related to this issue once the anno-
tation of implicit connectives in the PDTB has been adjudicated, at which point
we can survey what relations annotators have taken to hold between ARG1 and
material intervening between it and its associated connective. Such empirical
evidence of something like intentional structure would be very exciting to find.
A second observation relates to other linguistic devices that convey discourse
relations, besides the discourse connectives annotated in the PDTB. In annotating
implicit connectives, we noticed different cases whose paraphrase in terms of an
explicit connective sounded redundant. A closer look revealed systematic non-
lexical indicators of discourse relations, including:
cases of S-initial PPs and adjuncts with anaphoric or deictic NPs such as at
the other end of the spectrum,adding to that speculation, which convey a re-
lation (eg,  and   in the above two cases)
in which the immediately preceding sentence provides a referent for the
anaphoric or deictic expression, which thereby makes it available as ARG1.
cases of equative be in which the immediately preceding sentence provides
a referent for an internal anaphoric argument of a relation-containing NP
in subject position – eg, the  relation conveyed by the effect is
(ie, the effect [of that]is) in
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[184]   
(12) The New York court also upheld a state law, passed in 1986, extending for
one year the statute of limitations on filing DES lawsuits.T  
 lawsuits that might have been barred because they were
filed too late could proceed because of the one-year extension.
Identification of these alternative ways of conveying discourse relations (labelled
 in the PDTB) will allow for a more complete annotation of discourse rela-
tions in other corpora, including corpora in languages other than English.4
A third observation relates to the distinction that Blühdorn (2007) has drawn
between discourse hierarchy associated with the pragmatic/intentional concept
of focality versus discourse hierarchy associated with the syntactic/semantic con-
cept of constituency. It is clear that both some sort of intentional structure and some
sort of informational structure are needed in discourse, just as was suggested in
the theories of Grosz and Sidner (1986), Moore and Pollack (1992), and Moser and
Moore (1996). It is just that the data seem to tell a somewhat different story of
the properties of these structures than those assumed in these earlier theories of
abstract discourse relations.
Our previous observation about the status of material intervening between
ARG1 and S-initial But and So related to a discourse hierarchy associated with fo-
cality, with the intervening material playing a supplemental role in the discourse.
Our observation here relates to constituency and is a consequence of the procedure
used in annotating the PDTB. This differed from the procedure used in annotating
the  corpus (Carlson et al. 2003), the  corpus (Polanyi et al. 2004) and the
GraphBank corpus (Wolf and Gibson 2005), where annotators first marked up the
text with a sequence of elementary discourse units that exhaustively covered it (just
as a sequence of words and punctuation exhaustively covers a sentence) and then
identified which of the units served as arguments to each relation. Instead, as
already noted, PDTB annotators were instructed to select the minimal clausal text
span needed to interpret each of the two arguments to each explicit and implicit
discourse connective.
We reported in (Dinesh et al. 2005) cases where the so-annotated arguments
to discourse connectives diverged from syntactic constituents within the Penn
TreeBank (PTB) — in particular, where instances of attribution (eg, “purchasing
[4] Independently of the PDTB, we also noticed that discourse relations could be conveyed by marked syntax
– for example, the expression of a  relation in the marked syntax of
(i) Had I known the Queen would be here, I would have dressed better.
or of a  in the marked syntax of
(ii) The more food you eat, the more weight you’ll gain.
Taking account of these examples will allow a more complete annotation of other corpora.
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  [185]
agents said” in Example (6) earlier and “analysts said” in Example (13)), headed
non-restrictive relatives (eg, “which couldn’t be confirmed” in Example (13)), and
headless non-restrictive relatives (eg, “led by Rep. Jack Brooks (D., Texas)” in
Example (14)), all of which are constituents within the PTB’s syntactic analyses,
are nevertheless not part of the discourse arguments of which they are syntactic
constituents.
(13) A traders rushed to buy futures contracts,many remained skep-
tical about the Brazilian development, which couldn’t be confirmed, analysts
said.
(14) Some Democrats, led by Rep. Jack Brooks (D., Texas), unsuccessfully opposed
the measure because they fear that the fees may not fully make up for the budget
cuts.
B Justice Department and FTC officials said they expect the filing fees
to make up for the budget reductions and possibly exceed them.
This suggests another way in which syntax and discourse may diverge, in addi-
tion to the lack of correspondence between coordination and subordination in
syntax and in discourse noted by Blühdorn (2007): Material that is part of a con-
stituent analysis in syntax may not be part of a constituent analysis in discourse.
Also relevant here are examples such as (4) above, where constituency structure
in syntax is standardly taken to be a tree, while in discourse, it appears to be a
simple DAG (with the main clause a constituent of two distinct discourse connec-
tives). Lee et al. (2006) presents an array of fairly complex constituency patterns
of spans within and across sentences that serve as arguments to different con-
nectives, as well as parts of sentences that don’t appear within the span of any
connective, explicit or implicit. The result is that the PDTB provides only a par-
tial but complexly-patterned cover of the corpus. Understanding what’s going on
and what it implies for discourse structure (and possibly syntactic structure as
well) is a challenge we’re currently trying to address.
[4] 
There is renewed interest in discourse structure, as more and larger annotated
discourse corpora are becoming available for analysis. While there is still much
to be gained from trying to extract as much as possible through machine learning
methods based on superficial features of discourse that we believe we understand,
there is also much to be gained from a deeper analysis of discourse structure that
suggests new features that we are only now beginning to discover.
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[186]   

We would like to thank Nikhil Dinesh, Aravind Joshi, Mark Steedman and Bergljot
Behrens for their comments on an earlier draft of this paper. The result of their
suggestions is a much more coherent and, to our minds, more interesting paper.

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  
Bonnie Webber
University of Edinburgh
2 Buccleuch Place
Edinburgh EH8 9LW
United Kingdom
bonnie@inf.ed.ac.uk
Rashmi Prasad
University of Pennsylvania
Institute for Research in Cognitive Science
3401 Walnut Street, Suite 400A
Philadelphia PA 19104-6228
USA
rjprasad@seas.upenn.edu
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... He proposed a multilevel analysis approach that would reconcile these issues. A variety of formalisms have been developed that would address limitations in RST (e.g., Asher & Lascarides, 2003;Webber & Prasad, 2009;Wolf & Gibson, 2005). An assumption made for this paper is that the theory and practice of RST is sufficiently well developed as to produce useful and interesting analyses. ...
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... Such structures allow us to further analyse the text. a) Discourse Structure: Discourse structure In layman's terms, the discourse can be seen as a "sentence of sentences" [23]. The authors of [24] use this understanding to split documents into important and not important spans of text that are then used to define the polarity of the entire document. ...
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... Theories such as RST start at the top, being concerned with the organization of discourse, and progress towards the bottom, towards an understanding of how that organization is conveyed in language. The two types of approaches can also be seen as part of the 'swings and roundabouts' thatWebber & Prasad (2009) discuss, as a set of swings in discourse research between theory-driven interpretations and data-driven analyses. Their account specifically mentions a swing from Halliday and Hasan in the 1970s, with a focus on data, to Mann and Thompson in the 1980s, who focused on theory, along many other approaches to coherence relations in that time(Grosz & Sidner 1986;Moser & Moore 1996;Polanyi 1988). ...
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... Innumerable other taxonomies have been proposed, see e.g. Danlos (2008), Irmer (2011), and Webber and Prasad (2009 for discussions. ...
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