Philip Windridge, Bernadette Sharp
Faculty of Computing, Engineering and Technology, Staffordshire University, Beaconside, Stafford, UK
Geoff Thompson
School of English, University of Liverpool, Liverpool, UK
Keywords: Knowledge Representation, taxonomy, system network transcript analysis, XML
Abstract: This paper introduces a design for the taxonomical re
presentation of participants’ instantial meaning-
making, as the basis for providing a measure of ambiguity and contestation. We use hyponymy and
meronymy as the basis for our taxonomies and adopt the System Network formalism as the basis for their
representation. We achieve an integration of transcript and taxonomy using an XML based ‘satellite’ system
of data storage. Content data forms a ‘Root’ document which can then ‘mapped’ to by an arbitrary number
of ‘Descriptor’ documents. This system represents instantial meanings by mapping Descriptor document
elements to elements in the Root. Part of this mapping also includes the sequence of Root elements,
accommodating the diachronic representation of meaning-making. This diachronic representation provides
the basis for measuring ambiguity and contestation.
This research is carried out as part of the Tracker
project (Rayson et al. 2003) which has the aim of
reducing rework through decision management. The
approach described in this paper works with
transcripts to analyse the active negotiation of
meanings. This negotiation can lead to varying
degrees of contestation and/or ambiguity between
participants in the social activity from which the
transcript is produced. Making this explicit, through
a comparison of participants’ meaning-making, has
the potential to augment summative records
associated with a decision, such as sets of minutes.
The purpose of this paper is to introduce Transcript
Based Taxonomies as a novel representation
We take a participant’s experiential meaning-
aking as the production and construal of semantic
associations between lexical units that are
attributable to that participant. This aspect of
meaning-making is revealed in the transcribed
utterances of participants. We identify semantic
associations forming dynamic taxonomical relations,
both within and between these utterances, as one
way of reflecting participants’ negotiation of
meanings. Contestation and/or ambiguity is shown
by comparing participants’ taxonomies where their
meaning-making is comparable. The effectiveness
and simplicity of this comparison is facilitated
through the integration of transcript and taxonomy
representations using Extensible Markup Language
(XML) (W3C 2004). Transcript Based Taxonomies
are the result of this integration.
In section 2 we provide a conceptual view of
script Based Taxonomies. In section 3 we
consider the form that this representation takes by
briefly discussing network based symbolic
knowledge representation. Section 4 describes the
representation of Transcript Based Taxonomies
using XML as an integration of transcript and
taxonomy. Finally, in the conclusion, we discuss this
representation in terms of our research requirements
and look to their future development.
The worked examples in this paper are extracted
m the transcript of one formal meeting involving
eight academics in total. Discussion in the meeting
emphasised how project tasks would be organised
and co-ordinated and included a general discussion
about resource management.
Windridge P., Sharp B. and Thompson G. (2005).
In Proceedings of the Seventh International Conference on Enterprise Information Systems, pages 267-272
DOI: 10.5220/0002521102670272
Transcript Based Taxonomies represent meaning-
making associated with a participant in a social
activity. They are concerned with the analysis of
transcripts for two reasons. The first is that the
transcript is the most practical way of accessing both
the record of what was spoken and the associated
contextual information affecting the meaning of
utterances. The second reason, given the practicality
of the transcript, is that it is the best means by which
the negotiation of meanings can be analysed.
Our analysis is based on taxonomy relations
between lexical units. Lexical units are realised by
the utterance of one or more words that express a
single concept; for example ‘lexical unit’ or
‘participant’s utterance’. Relations between lexical
units are part of the semantic cohesiveness of the
transcript and form lexical chains that can be
associated with a participant. The particular types of
lexical relation that we concentrate upon are
hyponymy and meronymy. The hyponymy relation
can be understood as the so-called is-a relation. In
Figure 1a ‘management meeting’ and ‘technical
meeting’ both have an is-a relation to ‘meetings’ and
are co-hyponymous to each other. The meronymy
relation can be understood as the part-of relation.
For example, in Figure 1b ‘set of activities’,
‘deliverables’ and ‘milestones’ have been analysed
as being part-of ‘project management’ and therefore
in a co-meronymous relation to each other. It must
be emphasised that the meaning-making we are
describing emphasises the taxonomical
representation of meanings as they are construed in
the transcript rather than referencing any pre-defined
and commonly accepted taxonomies. The meanings
that we derive from the transcript are reflected in a
taxonomical representation which is based upon this
transcript analysis.
The lexical units used in forming Transcript
Based Taxonomies are identified for their function
in experiential meaning. Experiential meaning
(Halliday 1978) can be understood as ‘meaning
based upon an interpretation of our experience’; in
contrast to meanings that establish and maintain
interpersonal relationships and meanings that have a
cohesive function in a given situation. Through
processes of self-reflection and communication
experience is reconstituted through time so that
experiential meanings change. We are apt to ‘change
our minds’ or contradict things that we have said in
the past. Transcript Based Taxonomies must
therefore be understood as dynamic.
The participant in a social activity is an
individual person to whom a taxonomy of lexical
units can be ascribed. This ascription does not
require the utterance of a lexical unit by a participant
but it does require that the participant ‘buy into’ or
accept their meaning. Figure 1b provides an example
of this. The participant ‘JCA’ is asking about
intended tasks and ‘PRA’ refers to existing tasks, but
it can be seen that there is overlap in their respective
lexical chains.
Transcript based taxonomies allow for the
measurement of ambiguity and contestation by
concentrating upon the description of relationships
between lexical units. Where taxonomies can be
derived for two or more participants on a given topic
their comparison provides a measure of their
contestation as contradictory lexical units and/or
lexical unit relationships. For instance, in Figure 1b
there is contestation surrounding ‘operational tasks’
where they have been analysed as either directly
associated with ‘project management’ for the
participant ‘JCA’ or with ‘bid’ for the participant
‘PRA’. Extracting the meaning-making of a
participant for a particular topic allows the analysis
of the participant’s meanings in terms of consistency
both in time and over time. Inconsistency within a
taxonomy representing temporally equivalent
meanings indicates the presence of contradiction as
ambiguity. For example, in Figure 1b ‘PRA’ appears
to accept “the intention to have…deliverables” that
‘JCA’ talks of but then contradicts this by stating
their existence in the bid as “a set of deliverables on
page 16”. There is a contrast between a proposal for
deliverables as an intention and a proposal for
deliverables as an actuality. This temporally
equivalent acceptance and contradiction adds a
degree of ambiguity to “deliverables” as part of
“bid” on the part of ‘PRA’.
Figure 1: Lexical chains showing taxonomy relations
b) Lexical chains showing meronymy (participant ‘JCA’
using black lines, ‘PRA’ using grey lines)
a) Lexical chain showing hyponymy
Transcript Based Taxonomies deal with symbols (as
lexical units), and their interrelationships, which
form a representation of participants’ meaning-
making. In Artificial Intelligence the development of
formalisms for this purpose are part of symbolic
knowledge representation. Discussing what a
representation is, Eysenck and Keane state that “it
stands for some thing in the absence of that thing”
(Eysenck & Keane 1995:204). Davis et al. (1993)
concur by saying it acts as a surrogate used for
focussing on particular aspects of a thing. The
knowledge aspect refers to the information that is
contained in the representation with no claim to its
veracity. As Reichgelt puts it, “the fact that some
piece of information has been written down in a
knowledge representation language does not by
itself make it true” (Reichgelt 1991:3); the
knowledge representation makes no claim about the
truth of the original data. This view of knowledge
representation accords with Transcript Based
Taxonomies to the extent that:
they are intended to represent that aspect of the
social activity concerned with linguistic
meaning-making, and
they do not seek to make claim to any objective
Transcript Based Taxonomies, in accordance
with any other knowledge representation, afford a
subjective viewpoint. This subjectivity can be seen
in the inevitable ontological commitments embodied
by the knowledge representation itself, in the way
knowledge is organised, and in how this
representation structure is populated with data when
it is instantiated. The discussion of the ontological
commitments embodied in Transcript Based
Taxonomies, and the discussion of instantiation, is
unfortunately beyond the scope of this paper and
forms the basis for future publications. Here we shall
simply state that Transcript Based Taxonomies are
not concerned with representing ‘objective’ or
commonly shared abstracted truth.
The taxonomical representation of lexical units
lends itself to a network representation such as
semantic networks. This form of symbolic
knowledge representation has already been used to
support natural language processing by embodying a
structural linguistic approach. Simmons, for
instance, considers semantic networks to be “a
computational theory of superficial verbal
understanding in humans” (Simmons 1973:63)
which he uses for the recognition and generation of a
subset of English sentence structures. Woods (1985)
goes further by indicating their use as a means of
understanding and modelling cognitive processes
where they offer a way of “representing the
meanings of sentences inside the brain (of humans
or other intellects) that is not merely a direct
encoding of the English word sequence” (Woods
1985:220). However, in the research reported here
we are not attempting to support natural language
processing, we are only concerned with representing
participants’ meaning-making as they occur in a
transcript. This greatly simplifies the task of
representation in terms of the number of
relationships that are required.
The network representation formalism adopted
in this research is called the system network (Martin
1992; Eggins 1994). Figure 2 shows the system
network version of the lexical chain examples taken
from Figure 1. System networks share many features
with semantic networks. At their most basic level
they are nodes connected by arcs that can be
grouped by the type of relationship they represent in
a similar way to the ‘and/or’ graph. A system
network can be comprised of one or more systems,
with each system defined by one or more entry
conditions and one or more outcomes. For instance,
in Figure 2a ‘project management’ is an example of
an entry condition while ‘set of activities’,
‘deliverables’ and ‘milestones’ are all outcomes. If
‘JCA’ had specified some intended ‘deliverables’
this would have formed a further system as part of
the same system network with the outcome
‘deliverables’ acting as its entry condition.
A system can either represent a meronymy
relation (‘part-of’) indicated by a brace as in Figure
2a or an hyponymy relation (‘is-a’) indicated by a
vertical straight line connector (square bracket) as in
Figure 2b. The use of parentheses, such as those
surrounding ‘set of activities’ in Figure 2a, indicates
that the node value was not directly uttered by the
Unfortunately, in this basic form, the system
Figure 2: System network taxonomy relations
b) Hyponymy relation
network is inadequate to the requirements of
Transcript Based Taxonomies because instantial
meaning is lost as soon as they are abstracted from
the transcript. The system network acts as a
surrogate for the transcript even as the transcript acts
as a surrogate for the social activity from which it is
produced. While it is not possible to have direct
access to a participant’s meanings, because of the
historically specific nature of discourse (Foucault
1972), the question is whether the transcript should
be viewed as an additional layer of abstraction or
whether it should be integral to the taxonomy
structure. Any form of representation introduces the
possibility of misconstrual. As pointed out by Davis
et al “representations are imperfect, and any
imperfection can be a source of error” (Davis et al.
1993:19). The burden on a taxonomy to faithfully
represent meaning-making is increased if the
transcript acts as an intermediary form of
representation. Integration of transcript and
taxonomy, thus removing a level of abstraction, will
act to minimise errors.
The integration of transcript and taxonomy in a
single representation structure is dependent upon the
way that the data is stored using XML. In the
transcript it relies upon the separation of what we
term the ‘core data content’, which is the written
representation of words uttered by the participants,
from data that adds meaning to or re-interprets the
utterances (metadata). The ‘SLA Descriptor’
contains transcript specific data other than uttered
words and is discussed in section 4.1. The
‘Taxonomy Descriptor’ contains the taxonomy
relations and is discussed in section 4.2.
4.1 Transcripts as XML
There are two main concerns for the representation
of social activities in transcripts:
1. That the representation should be as machine
processable as possible,
2. That the representation should retain the
‘situatedness’ of meaning-making, including
contextual data.
These concerns are not specific to the current
research and have been addressed by a number of
transcription standards. CHAT (MacWhinney 2004),
for instance, is a transcription standard designed to
improve the reliability and shareability of transcripts
through a common, selectable and exhaustive
notation. It allows the inclusion of metadata to be
associated with a transcript in its entirety or to
particular parts down to the lexical unit or below to
the word or morpheme. This inclusion of metadata
facilitates both human and automated linguistic
analysis and processing.
The representation of CHAT transcript data in
XML has been described in Clarke et al. (2003) and
will only briefly be outlined here. The dependency
of the SLA Descriptor on the Root document has
already been mentioned in general terms above. This
dependency takes the form of a link that ‘maps’ the
SLA Descriptor to the Root; Figure 3 shows an
example of how this is achieved. The Root
document uses the <w> element to hold individual
words and the SLA Descriptor maps onto these
elements through their element number (in this case
the element numbers have been added to the Root
document for illustration purposes only). The co-
ordinates for this mapping are identified using the
attributes ‘beg’ and ‘len’. The Root and SLA
Descriptor documents contain enough information to
provide a ‘view’ of the data that conforms to the
CHAT transcription standard.
4.2 Taxonomies as XML
The XML taxonomy representation views the
System Network as comprised of distinct but
interconnected systems. This means that arbitrarily
complex System Network structures can be
represented. Figure 4 shows a simple ‘single system
example first introduced in Figure 2a above. The
example illustrates the scaleable notation that has
been used which is based upon a simple ‘entry
conditions and outcomes’ template for each system.
Systems are represented by separate <system>
elements under the parent element
ure 3: Reconstructin
CHAT transcri
t from XML
<systemNetwork>. Adding systems to the
System Network is achieved by an outcome
reference being used as an entry condition reference
for a connected system. Each system contains one or
more entry conditions within the <entry> element
and one or more outcomes within the <outcome>
element. The <outcome> element can either have
an ‘reltype’ attribute value of ‘mer’ indicating
meronymy or a value of ‘hyp’ indicating hyponymy.
Both <entry> and <outcome> elements can
contain <lexicalItem> or <placeholder>
elements. The element can either reference a lexical
unit that has been bought into by the participant or,
in certain circumstances, a lexical unit that has been
analysed as ‘missing’ from the text, for example
where an object is referenced through ostension. As
can be seen in Figure 4 <lexicalItem> and
<placeholder> directly reference sections of
the Root document using the ‘beg’ and ‘len
attributes in the same way as described in the
previous section. Using this system of pointers to
record where the lexical unit originates within the
transcript has two effects:
1. Sequence and proximity of lexical units are
governed by their mappings to the Root
document, addressing the requirement of
representing diachronic meaning-making.
2. Any contextual data associated with a location in
Root can be accessed in the SLA Descriptor
‘view’; the same applies for the use of any other
Descriptor document as a part of the
However, in order for the Taxonomy Descriptor
to show contestation it is necessary to extend our
view of the system network. Specifically we need to
show where meanings are contradictory rather than
the legitimate ‘mutual exclusivity’ of meanings
which exists in the co-hyponymy relation. There are
four methods that we have adopted for doing this.
Firstly, where the meaning of an entry condition
is contested between two or more participants, a
‘contesting’ <placeholder> element references
the lexical unit for all participants except the original
utterer where a ‘contesting’ <lexicalItem>
element serves this purpose. An example of this is
shown in Figure 5, following Figure 1b, where the
meaning of ‘project management’ appears to be
introduced by ‘AAL’ referring to hierarchical lines
of communication in the project, ‘JCA’ subsequently
appears to interpret the same lexical unit as referring
to organised activities of the project.
Secondly, where the meaning of an outcome is
contested between two or more participants a
‘contesting’ <placeholder> or
<lexicalItem> element is used as the outcome.
Thirdly, where the meaning of an entry condition
is contested by the same participant a ‘contesting’
<lexicalItem> element ‘re-presents’ the entry
condition for a new system.
Lastly, where the outcomes of a system are
contested by the same participant a new system is
created with an identical entry condition to the
Effectively, the final two methods create parallel
interpretations in the Taxonomy Descriptor. These
interpretations are distinguished by the sequential
occurrence of their lexical units in the Root
document. In a similar approach, the representation
of ambiguity is achieved by using an ‘ambiguity’
<placeholder> or <lexicalItem> element
where appropriate.
Figure 4: A System Network ‘view’ derived from a
Taxonomy Descriptor
a) Taxonomy Descriptor of participant ‘AAL’
b) Taxonomy Descriptor of participant ‘JCA’
Figure 5: Contested network entry conditions
In this paper we have discussed the representation of
social activities as a taxonomy of instantial
meanings which have been derived from transcript
analyses. We have highlighted a number of factors
that have formed the basis for this representation and
have indicated their relationship to network based
symbolic knowledge representation. We then
outlined our XML based ‘satellite system’ of storing
data and showed how this system is the basis for
Transcript Based Taxonomies. Finally we showed
how the Taxonomy Descriptor supports a number of
methods for the representation of contestation and
ambiguity which, together with the ability to
represent the sequential development of meaning-
making (see section 4.3 and the brief discussion
below), provides the basis for their measure.
It has been pointed out that, due to the
historically specific nature of participants’ meaning-
making, in addition to what can be described as
subjective interpretation, it is impossible to faithfully
capture intended meanings. Whilst we have accepted
this limitation we have also removed an unnecessary
intermediate layer of abstraction by integrating the
transcript and taxonomy layers using the satellite
system of XML documents. This means that the
taxonomy can take full advantage of the exhaustive
notation, and possibility for simplified machine
processing, offered by the CHAT transcription
standard. In directly mapping either an entry
condition or an outcome to elements in the Root
document they become identified with the instantial
meanings provided by the SLA Descriptor ‘view’
(section 4.3). This instantial meaning is unique and
any Taxonomy Descriptor that uses this mapping
offers a direct and unequivocal comparison with any
other Taxonomy Descriptor that maps the same
point. Furthermore, this mapping carries with it a
sequential order of appearance of elements in the
Root document that affords a dynamic
representation of meaning-making.
The primary task of Transcript Based
Taxonomies is to provide a means for the
comparison of meaning-making and this carries the
concomitant requirement that lexical units should be
associable with their synonyms, antonyms, etc., as
they occur within the transcript. The association of
Transcript Based Taxonomies to separate
participants means that accounting for instantial
synonymy, antonymy, etc., is vital for a valid
comparison to take place; participants may use
different words to describe the same thing, or they
may use a word to directly contest another.
Development of this analysis will increase the
delicacy of our representation.
This work was conducted under the auspices of
the Tracker Project, UK EPSRC grant
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