XML/XSL in the Dictionary: The Case of Discourse Markers
Daniela Berger and David Reitter and Manfred Stede
University of Potsdam
Dept. of Linguistics / Applied Computational Linguistics
P.O. Box 601553 / D-14415 Potsdam / Germany
fberger|reitter|stedeg@ling.uni-potsdam.de
Abstract
We describe our ongoing work on an application
of XML/XSL technology to a dictionary, from
whose source representation various views for
the human reader as well as for automatic text
generation and understanding are derived. Our
case study is a dictionary of discourse markers,
the words (often, but not always, conjunctions)
that signal the presence of a disocurse relation
between adjacent spans of text.
1 Overview
Electronic dictionaries have made extensive use
of SGML encoding in the past, but to our
knowledge, the advantages of contemporary
frameworks such as XML/XSL are only be-
ginning to be explored. We are applying this
framework to our work on a lexicon of ‘dis-
course markers’ and will outline the advantages
of deriving a variety of views from the common
underlying lexical resource: di erent views for
di erent demands by the human eye, but also
application-speci c views that tailor the dictio-
nary to either the parsing or the generation task
(a third one would be machine translation but
is not covered in this paper), and that respect
the conventions of the speci c underlying pro-
gramming language. Using XSL style sheets for
producing the views automatically is especially
useful when the lexicon is still under develop-
ment: the rami cations of particular modi ca-
tions or extensions can be made visible easily
by running the conversion and testing the ap-
plications in question.
Discourse markers are words (predominantly
conjunctions) that signal the kind of semantic or
rhetorical relationship between adjacent spans
of text. In text generation, when given a rep-
resentation of propositions and relations hold-
ing between them, the task is to select an ap-
propriate discourse marker in the context. In
text understanding, discourse markers are the
most important clues for inferring the ‘rhetori-
cal structure’ of the text, a task that has lately
been called ‘rhetorical parsing’. While these
discourse markers are a somewhat idiosyncratic
class of lexical items, we believe that our general
approach to applying XML/XSL can be fruitful
to other branches of the dictionary as well (in
particular to the open-class \content words").
After reviewing some earlier work on XML-
based dictionaries (Section 2) and discussing the
notion of discourse markers (Section 3), we pro-
ceed to outline the particular requirements on
a discourse marker lexicon from both the text
generation and the text understanding perspec-
tive (Section 4). Then, Section 5 describes our
XML/XSL encoding of the source lexicon and
the views for the human eye, for automatic text
generation, and for text understanding. Finally,
Section 6 draws some conclusions.
2 Dictionaries and XML: Related
work
Recent research in lexicology has been focused
on two di erent goals: the mark-up process
for existing print dictionaries, and the success-
ful construction of machine-readable dictionar-
ies from scratch.
The  rst approach has received more at-
tention in the past. This is partly due to
the fact that the transformation of existing
print dictionaries into modules for NLP applica-
tions promises to be less time-consuming than
the construction of a new machine-readable
database. Lexicologists agree on the fact that a
dictionary entry is inherently hierarchical, i.e.,
it consists out of atomic elements grouped to-
gether within non-atomic elements in a tree-like
hierarchy. Many approaches place orthograph-
ical and phonological information together in
one group, while grammatical information is put
in a di erent group. This hierarchical approach
also allows to denote scope by inserting informa-
tion at di erent levels of the hierarchy. Again,
information about orthography and phonology
generally applies to every facet of the headword
and are thus placed high in the hierarchy, while
other information might only apply to single
de nitions and thus ranks lower hierarchically
(Amsler/Tompa, 1988; Ide, V eronis, 1995; Ide
et al., 2000).
A common problem of lexicologists working
with print dictionaries is the fact that there is
a certain variation between entries in any two
given dictionaries or even within the same dic-
tionary. This results in a neccessary trade-o 
between the descriptive power and the gener-
ality of an approach, i.e. to design a SGML
application that is both descriptive enough to
be of practical value and general enough to ac-
comodate the variation.
There has been, on the other hand, only little
research on machine-readable dictionaries that
are not based on print dictionaries. To our
knowledge, only (Ide et al., 1993) deals with
this issue by reviewing several approaches to-
wards encoding machine-readable dictionaries.
One of these is the use of text models that ap-
ply a rather  at hierarchy to mark up dictio-
nary entries. These text models might chie y
use typographical or grammatical information.
Another approach is using relational databases,
in which the information contained in a dictio-
nary entry is distributed over several databases.
A third approach is based on feature structures
that impose a rich hierarchical structure on the
data. The authors  nally describe an example
application that uses feature structures encoded
in SGML to set up a machine-readable dictio-
nary.
The papers mentioned above agree on us-
ing SGML for the mark-up. We found that
their SGML code is, however, in general XML-
compliant.
3 Discourse markers
Several contemporary discourse theories posit
that important aspects of a text’s coherence
can be formally described (and represented) by
means of discourse relations holding between
adjacent spans of text (e.g. Asher, 1993; Mann,
Thompson, 1988). We use the term discourse
marker for those lexical items that (in addition
to non-lexical means such as punctuation, as-
pectual and focus shifts, etc.) can signal the
presence of such a relation at the linguistic sur-
face. Typically, a discourse relation is associ-
ated with a wide range of such markers; con-
sider, for instance, the following variety of Con-
cessions, which all express the same underly-
ing propositional content. The words that we
treat as discourse markers are underlined.
We were in SoHo;fneverthelessjnonetheless
j however j still j yetg, we found a cheap bar.
We were in SoHo, but we found a cheap bar
anyway.
Despite the fact that we were in SoHo, we
found a cheap bar.
Notwithstanding the fact that we were in
SoHo, we found a cheap bar.
Although we were in SoHo, we found a cheap
bar.
If one accepts these sentences as paraphrases,
then the various discourse markers all need to
be associated with the information that they
signal a concessive relationship between the two
propositions involved. Notice that the markers
belong to di erent syntactic categories and thus
impose quite di erent syntactic constraints on
their environment in the sentence. Discourse
markers do not form a homogeneous class from
the syntactican’s viewpoint, but from a func-
tional perspective they should nonetheless be
treated as alternatives in a paradigmatic choice.
A detailled characterization of discourse
markers, together with a test procedure for
identifying them in text, has been provided for
English by (Knott, 1996). Recently, (Grote, to
appear) adapted Knott’s procedure for the Ger-
man language. Very brie y, to identify a dis-
course marker (e.g., because) in a text, isolate
the clause containing a candidate from the text,
resolve any anaphors and make elided items ex-
plicit; if the resulting text is incomplete (e.g.,
because the woman bought a Macintosh), then
the candidate is indeed a ‘relational phrase’, or
for our purposes, a two-place discourse marker.
In addition to the syntactic features, the dif-
ferences in meaning and style between similar
markers need to be discerned; one such di er-
ence is the degree of speci city: for example,
but can mark a general Contrast or a more
speci c Concession. Another one is the no-
table di erence in formality between, say but ...
anyway and notwithstanding.
From the perspective of text generation, not
all paraphrases listed above are equally felici-
tous in speci c contexts. In order to choose
the most appropriate variant, a generator needs
knowledge about the  ne-grained di erences be-
tween similar markers for the same relation.
Furthermore, it needs to account for the interac-
tions between marker choice and other genera-
tion decisions and hence needs knowledge about
the syntagmatic constraints associated with dif-
ferent markers. We will discuss this perspective
in Section 4.1
From the perspective of text understanding,
discourse markers can be used as one source of
information for guessing the rhetorical structure
of a text, or automatic rhetorical parsing. We
will characterize this application in Section 4.2.
4 Requirements on a discourse
marker lexicon
As the following two subsections will show, text
generation and understanding have quite dif-
ferent preferences on the information coded in
a discourse marker lexicon, or \DiMLex" for
short. In addition, di erent systems employ dif-
ferent programming languages, and the format
of the lexicon has to be adapted accordingly.
Yet we want to avoid coding di erent lexicons
manually and thus seek a common \core rep-
resentation" for DiMLex from which the var-
ious application-speci c instantiations can be
derived. Before proposing such a representa-
tion, though, we have to examine in more detail
the di erent requirements.
4.1 The text generation perspective
Present text generation systems are typically
not very good at choosing discourse mark-
ers. Even though a few systems have incor-
porated some more sophisticated mappings for
speci c relations (e.g., in DRAFTER (Paris et
al., 1995)), there is still a general tendency to
treat discourse marker selection as a task to
be performed as a \side e ect" by the gram-
mar, much like for other function words such as
prepositions.
To improve this situation, we propose to view
discourse marker selection as one subtask of the
general lexical choice process, so that | to con-
tinue the example given above | one or another
form of Concession can be produced in the
light of the speci c utterance parameters and
the context. Obviously, marker selection also
includes the decision whether to use any marker
at all or leave the relation implicit. When these
decisions can be systematically controlled, the
text can be tailored much better to the speci c
goals of the generation process.
The generation task imposes a particular view
of the information coded in DiMLex: the en-
try point to the lexicon is the discourse relation
to be realized, and the lookup yields the range
of alternatives. But many markers have more
semantic and pragmatic constraints associated
with them, which have to be veri ed in the gen-
erator’s input representation for the marker to
be a candidate. Then, discourse markers place
(predominantly syntactic) constraints on their
immediate context, which a ects the interac-
tions between marker choice and other realiza-
tion decisions. And  nally, markers that are still
equivalent after evaluating these constraints are
subject to a choice process that can utilize pref-
erential (e.g. stylistic or length-based) criteria.
Therefore, under the generation view, the infor-
mation in DiMLex is grouped into the following
three classes:
| Applicability conditions: The necessary
conditions for using a discourse marker, i.e., the
features or structural con gurations that need
to be present in the input speci cation.
| Syntagmatic constraints: The constraints
regarding the combination of a marker and the
neighbouring constituents; most of them are
syntactic and appear at the beginning of the list
given above (part of speech, linear order, etc.).
| Paradigmatic features: Features that label
the di erences between similar markers sharing
the same applicability conditions, such as stylis-
tic features and degrees of emphasis.
Very brie y, we see discourse marker choice
as one aspect of the sentence planning task
(e.g. (Wanner, Hovy, 1996)). In order to
account for the intricate interactions between
marker choice and other generation decisions,
the idea is to employ DiMLex as a declara-
tive resource supporting the sentence planning
process, which comprises determining sentence
boundaries and sentence structure, linear order-
ing of constituents (e.g. thematizations), and
lexical choice. All these decisions are heavily
interdependent, and in order to produce truly
adequate text, the various realization options
need to be weighted against each other (in con-
trast to a simple,  xed sequence of making the
types of decisions), which presupposes a  exible
computational mechanism based on resources
as declarative as possible. This generation ap-
proach is described in more detail in (Grote,
Stede, 1998).
4.2 The text understanding perspective
In text understanding, discourse markers serve
as cues for inferring the rhetorical or semantic
structure of the text. In the approach proposed
in (Marcu, 1997), for example, the presence of
discourse markers is used to hypothesize indi-
vidual textual units and relations holding be-
tween them. Then, the overall discourse struc-
ture tree is built using constraint satisfaction
techniques. Our analysis method uses the lexi-
con for an initial identi cation and disambigua-
tion of discourse markers. They serve as one
of several other shallow features that determine
through a statistical, learned language model
the optimal rhetorical analysis.
In contrast to the use of markers in genera-
tion, the list of cues is signi cantly longer and
includes phrasal items like aus diesem Grund
(for this reason) or genauer genommen (more
precisely).
5 Our XML/XSL solution
In the following we show some sample represen-
tations and style sheets that have been abridged
for presentation purposes.
5.1 Source representation
In our hierarchical XML structure, the
<dictionary> root tag encloses the entire  le,
and every single entry rests in an <entry>
tag, which unambigously identi es every entry
with its id attribute. Within the <entry> tag
there are four subordinate tags: <form>, <syn>,
<sem>, and <examples>.
The <form> tag contains the orthographic
form of the headword; at present this amounts
to two slots for alternative orthographies. The
<syn> area contains the syntactic information
about the headword. In this shortened exam-
ple, there is only the <init field> tag present,
<?xml version="1.0" ?>
<?xml-stylesheet type="text/xsl"
href="short_dictionary.xsl" ?>
<!DOCTYPE dictionary SYSTEM "DTD.dtd">
5 <dictionary>
<entry id="05">
<form>
<orth>denn</orth>
<alt_orth>none</alt_orth>
10 <!-- . . . -->
</form>
<syn>
<init_field>-</init_field>
<!-- . . . -->
15 </syn>
<sem>
<function>causal</function>
<!-- . . . -->
</sem>
20 <examples>
<example>Das Konzert muss ausfallen,
*denn* die S&auml;ngerin ist erkrankt.
</example>
<example>Die Blumen auf dem Balkon sind
25 erfroren, *denn* es hat heute nacht
Frost gegeben.</example>
</examples>
</entry>
<entry>
30 <!-- more entries -->
</entry>
</dictionary>
Figure 1: The XML structure
which shows whether the headword can be used
in the initial  eld of a sentence. Correspond-
ingly, <sem> contains semantic features such as
the <function> tag, which contains the seman-
tic/discourse relation expressed by the head-
word. Finally, <examples>, contains one or
more <example> tags that may each give an ex-
ample sentence.
We have shortened this presentation consider-
ably; the full lexicon contains more  ne-grained
features for all three areas: within <form>, in-
formation on pronounciation, syllable structure,
and hyphenation; within <syn>, information
about syntactic subcategorization and possible
positions in the clause; within <sem>, for exam-
ple the feature whether the information subor-
dinated by the marker is presupposed or not.
5.2 HTML views
The listing in Figure 4 shows a style sheet that
provides an HTML by listing the XML data in
a format that roughly resembles a print dictio-
nary. Figure 2 shows the output that results
from applying this XSL  le to the XML source
in  gure 1.
05: denn
Occurrences: middle  eld / Nullstelle
Semantics: kausal
Related markers: weil da
Examples: Das Konzert muss ausfallen, *denn* die
S angerin ist erkrankt.
Die Blumen auf dem Balkon sind erfroren, *denn* es hat
heute nacht Frost gegeben.
Figure 2: One HTML view of the data
We assume that the general structure of the
formatting part of XSL is familiar to the reader.
We would like to highlight some details.
XLINK is used to ensure that the entry con-
tains an HTML-anchor named after the head-
word (ll. 14-20). This way it is possible to link
to a certain entry from the <rel> tag of a dif-
ferent entry (39-45).
We also employ the XSL equivalent to
an if/then/else construct (24-31). The
<xsl:choose> tag encloses the choices to be
made. The <xsl:when> tag contains the con-
dition match=".[alt orth=’none’]" that does
nothing if the <alt orth> tag contains the
data none. Every other case is covered by
the <xsl:otherwise> tag that prints out the
<alt orth> information if it is not no entry.
Entry alt orth init field mid field . . .
denn none - + . . .
da none + + . . .
zumal none - - . . .
weil none - - . . .
als none - - . . .
Figure 3: Another possible HTML view of the
data
Figure 3 shows another possible view for the
data. In this case the data is presented in table
form, ordered by the value of the mid field tag.
It would be easy to show that it is possible to
use a <xsl:choose> construct as shown in the
example before to print out only those entries
that satisfy a certain condition.
5.3 The text generation view
For the lexicon to be applied in our text
generation system ‘Polibox’ (Stede, 2002),
we need a Lisp-based version of DiM-
Lex. Using the (defstruct <name> <slot1>
<?xml version="1.0"?>
<xsl:stylesheet
xmlns:xsl=
"http://www.w3.org/1999/XSL/Transform">
5 <xsl:template match="/">
<FONT SIZE="-2">
<xsl:apply-templates/>
</FONT>
</xsl:template>
10 <xsl:template match="dictionary">
<xsl:apply-templates/>
</xsl:template>
<xsl:template match="entry">
<P><font size="2"><B><A>
15 <xsl:attribute name="NAME">
<xsl:value-of select="form/orth"/>
</xsl:attribute>
<xsl:value-of select="./@id"/>:
<xsl:value-of select="form/orth"/>
20 </A></b></font>
<xsl:apply-templates/></P>
</xsl:template>
<xsl:template match="form">
<xsl:choose>
25 <xsl:when match=".[alt_orth=’none’]">
</xsl:when>
<xsl:otherwise>
<BR/><B>Alternative orthography:</B>
<xsl:value-of select="alt_orth"/>
30 </xsl:otherwise>
</xsl:choose>
</xsl:template>
<xsl:template match="sem">
<BR/><B>Semantics:</B>
35 <xsl:value-of select="ko_sub"/>
/ <xsl:value-of select="function"/>
<br/><b>Related markers:</b>
<xsl:for-each select="rel">
<A><xsl:attribute name="HREF">
40 #<xsl:value-of select="."/>
</xsl:attribute>
<xsl:value-of select="."/></A>
</xsl:for-each>
</xsl:template>
45 <xsl:template match="syn">
<BR/><B>Occurrences:</B>
<xsl:choose>
<xsl:when match=".[init\_field=’-’]">
</xsl:when>
50 <xsl:otherwise>
initial field /
</xsl:otherwise>
</xsl:choose>
</xsl:template>
55 <xsl:template match="examples">
<BR/><B>Examples:</B>
<xsl:for-each select="example">
<xsl:value-of select="."/><BR/>
</xsl:for-each>
60 </xsl:template>
</xsl:stylesheet>
Figure 4: The XSL  le for the HTML view
shown in Figure 2
.. <slotn>) construct, we de ne a class of ob-
jects for discourse markers, where the features
needed for generation are stored in the slots.
Again, we abbreviate slightly:
(defstruct DiscMarker
Relation N-Complexity S-Complexity
Ortho POS ... Style)
Now, a Lisp-object for each individual dis-
course marker entry is created with the func-
tion make-Discmarker, which provides the val-
ues for the slots. Figure 5 shows the shape of the
entry for denn, whose XML-source was given in
 gure 1.
Again, we aim at deriving these entries au-
tomatically via an XSL sheet (which we do not
show here). Notice that the mapping task is
now somewhat di erent from the HTML cases,
since the transformation part of XSL (XSLT)
comes into play here. Instead of merely display-
ing the data in a web browser as in the examples
before, an XSLT processor may transform data
for use in some XML based client application.
As explained in Section 4.1, in the generation
scenario we are given a tree fragment consist-
ing of a discourse relation node and two daugh-
ters representing the related material, the nu-
cleus and the satellite of the relation. In order
to decide whether a particular marker can be
used, one important constraint is the \size" of
the daughter material, which can be a single
proposition or an entire sub-tree. The gener-
ator needs to estimate whether it will  t into
a single phrase, clause, sentence, or into a se-
quence of sentences; a subordinating conjunc-
tion, for instance, can only be used if the ma-
terial can be expressed within a clause. Thus,
the Lisp-entry contains slots N-Complexity and
S-Complexity, which are highly application-
speci c and thus do not have a simple corre-
sponding feature in the XML source represen-
tation of the dictionary. The XSL sheet thus
inspects certain combinations of daughter at-
tributes of <syn> and maps them to new names
for the  llers of the two Complexity slots in
the Lisp structure. (Similar mappings occur in
other places as well, which we do not show here.)
5.4 The text understanding view
Our analysis method recasts rhetorical parsing
as a set of classi cation decisions, where a pars-
(make-Discmarker
:Relation cause
:N-Complexity sent
:S-Complexity sent
5 :Ortho denn
:POS coordconj
:Style unmarked)
Figure 5: Lisp-version of generation-oriented
dictionary entry for denn (abridged)
ing framework builds a tree structured analy-
sis. Each of the decisions is based on a set of
features. Feature types range from syntactical
con guration to the presence of a certain dis-
course marker. The mapping from a pattern of
observed features to a rhetorical relation may be
learned automatically by a classi cation learn-
ing algorithm.
Learning and analysis applications use a pars-
ing framework that gives us a set of text span
pairs. Every two text spans are subject to a
classi cation learning algorithm (during train-
ing) or the actual classi er. So, a rhetorical rela-
tion is assigned to these two spans of text along
with a score so that the parsing framework may
decide which of several competing classi cations
to accept.
Learning and actual rhetorical analysis are
accomplished by a set of distinct tools that add
speci c annotations to a given input text, be-
fore resulting relations are learned or guessed.
These tools include a data mining component, a
part-of-speech tagger and a segmenter. They all
access data organized in an XML syntax. The
central learning and parsing application makes
use of a Document Object Model (DOM) repre-
sentation of the corpus. This data structure is
e ectively used for information interchange be-
tween several components, because it allows us
to easily visualize and modify the current data
at each processing step during development.
With the present corpus data, the learning al-
gorithm is theoretically able to identify rhetori-
cal markers automatically and could thus com-
pile a marker lexicon. However, markers are
highly ambiguous. Even though many of them
can be tagged as adverbials or conjunctions,
markers often lack distinctive syntactic and/or
positional properties; some of them are phrasal,
some are discontinuous. To identify signi cant
cue - relation correlations, a lot of annotated
data is necessary: more than is usually avail-
able. In a sparse data situation, we want to
easen the learning task for the rhetorical lan-
guage model: It makes sense to use a discourse
marker lexicon.
On the other hand, we do not expect a hand-
crafted lexicon to contain all contextual con-
straints that would enable us to assign a sin-
gle rhetorical relation. These constraints can be
very subtle; some of them should be represented
as probabilistic scalar information.
Thus, DiMLex contributes to initial dis-
course marker disambiguation. From each en-
try, we interpret syntactic positioning informa-
tion, morphosyntactic contextual information
and a scope class (sentential, phrasal, discourse-
level) as a conjunction of constraints. The pres-
ence of a certain discourse marker in a speci ed
con guration is one of the features to be ob-
served in the text.
Depending on the depth of the syntactic and
semantic analysis carried out by the text un-
derstanding system, di erent features provided
by DiMLex can be taken into account. Certain
structural con gurations can be tested with-
out any deep understanding; for instance, the
German marker w ahrend is generally ambigu-
ous between a Contrast and a Temporal-
Cooccurrence reading, but when followed by
a noun phrase, only the latter reading is avail-
able (w ahrend corresponds not only to the En-
glish while but also to during).
In the parsing client application, DiMLex
serves as resource for the identi cation of cue
phrases in speci c structural con gurations.
Rhetorical information from the DiMLex entries
may serve as one of several cues for the classi-
 cation engine. The  nal linking from cue pat-
terns to rhetorical relations is learned from a
corpus annotated with rhetorical structures.
6 Summary
We have presented our ongoing work on con-
structing an XML-based dictionary of discourse
markers, from which a variety of views are de-
rived by XSL sheets: For the dictionary de-
signer or application developer, we present the
dictionary in tabular form or in a form resem-
bling print dictionaries, but with hyperlinks in-
cluded for easy cross-referencing. Similarly, text
generation and understanding systems are on
the one hand written in di erent programming
languages and thus expect di erent dictionary
formats; on the other hand, the information
needed for generation and parsing is also not
identical, which is accounted for by the XSL
sheets. Evaluation of the approach will depend
on the client applications. Their implementa-
tion will determine the  nal shape of DiMLex.

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eration, Hearstmonceux Castle, 1996.
Web References
Domain Object Model
W3C Recommendation, 13 November 2000
http://www.w3.org/TR/DOM-Level-2-Core
Extensible Stylesheet Language (XSL) 1.0
W3C Recommendation, 15 October 2001
http://www.w3.org/TR/xsl
XML Base
W3C Recommendation 27 June 2001
http://www.w3.org/TR/xmlbase
XSL Transformations (XSLT) 1.0
W3C Recommendation, 16 November 1999
http://www.w3.org/TR/xslt
