Tracker Text Segmentation Approach: Integrating
Complex Lexical and Conversation Cue Features
C. Chibelushi
and B. Sharp
School of Computing and IT, University of Wolverhampton
Technology Centre (MI Building), Room MI 317, City Campus - South
Wulfrana, Street, Wolverhampton. WV1 1SB, U.K.
Faculty of Computing Engineering and Technology, Staffordshire University, U.K.
Abstract. While text segmentation is a topic which has received a great atten-
tion since 9/11, most of current research projects remain focused on expository
texts, stories and broadcast news. Current segmentation methods are well suited
for written and structured texts making use of their distinctive macro-level
structures. Text segmentation of transcribed multi-party conversation presents a
different challenge given the lack of linguistic features such as headings, para-
graph, and well formed sentences. This paper describes an algorithm suited for
transcribed meeting conversations combining semantically complex lexical re-
lations with conversational cue phrases to build lexical chains in determining
topic boundaries.
1 Introduction
The problem of text segmentation has been the recent focus of many researchers as
more and more applications require the tracking of topics whether for summarization,
classification and/or retrieval tasks of textual documents. Since 9/11 text segmenta-
tion became a common technique used to detect the threads contained in instant mes-
saging and internet chat forums for various applications, including information re-
trieval, expert recognition and even crime prevention [3].Text segmentation can be
carried out on audio, video, and textual data. The aim of segmentation is to partition
a text into contiguous segments related to different topics. The increasing interest in
segmenting conversations is mainly explained by the number of its applications as
outlined in Table 1, whose granularity level of the segmentation depends on the size
of the chosen units of analysis which varied from utterances to words to phrases.
In this paper we present an algorithm for text segmentation relevant to transcribed
meetings involving a multi-party conversation. While previous research has focused
mostly on structured texts, broadcast news, and monologues which consist of cohe-
sive stories, our corpus consists of 17 manually transcribed meeting conversations. It
includes incomplete sentences, sentences related to social chatting, interruptions, and
references by participants made to visual context. Consequently, the analysis of our
transcripts poses an additional complexity due to their informal style, the use of visual
Chibelushi C. and Sharp B. (2008).
Tracker Text Segmentation Approach: Integrating Complex Lexical and Conversation Cue Features.
In Proceedings of the 5th International Workshop on Natural Language Processing and Cognitive Science, pages 104-113
DOI: 10.5220/0001740501040113
Table 1. A review of the language processing applications to transcripts [5].
cues, and the lack of macro-level text units such as headings, paragraphs as well as
their spontaneous and often argumentative nature.
The motivation for our research project stems from the need to analyse a set of tran-
scribed meetings with the view to track a set of decisions and their associated issues
and actions discussed in the meetings in relation to software development. These
elements are then fed into a database to provide a tracking system to support software
development in identifying the decisions made at these meetings and gaining an un-
derstanding of the issues and decisions that may have led to any unnecessary rework.
In this paper we begin by reviewing the methodologies associated with text segmen-
tation, and we describe our Tracker Text Segmentation (TTS) approach to segment-
ing transcribed meeting conversations. Finally we discuss the results and the limita-
tions of our algorithm, and conclude our research outlining future research directions.
2 Previous Work
A review of the literature on text segmentation techniques reveals two distinct ap-
proaches: statistically based and linguistically driven methods [5, 14]. Some statistical
approaches are based on probability distributions [2], machine learning techniques
ranging from neural networks [4], to support vector machines [18] and Bayesian
networks [21], while others treat text as an unlabelled sequence of topics using a
hidden Markov model [24]. [8] developed a text segmentation tool called C99 which
uses a divisive clustering algorithm developed by [20] to identify topic boundaries.
The other text segmentation approach is derived from the lexical cohesion theory of
[9] and uses terms repetition to detect topic changes [25, 19, 10] n-gram word or
phrases [12], or word frequency [20, 1]. Some use lexical chains to identify topic
changes [10, 22], or prosodic clues to mark shifts to new topics [13, 19]. However
most lexical cohesion-based segmentation approaches use lexical repetition as a form
of cohesion and ignore the other types of lexical cohesion such as synonym, hyper-
nymy, hyponymy, meronymy [23]. A different approach is adopted by [16] who
combine decision trees with linguistic features extracted from spoken texts.
The above segmentation methods are well suited for written and structured texts mak-
ing use of their distinctive macro-level structures which are deficient in transcribed
texts. In the study of our transcripts the topic boundaries are often fuzzy, some topics
are re-visited at different stages of the meeting, and do not always follow the intended
agenda, rendering the segmentation process a very challenging task. As a result we
needed to develop a segmentation method which could handle the complexity and the
lack of structure but building on the macro-level structures pertinent to transcribed
texts such as the notion of utterance, the spontaneous speech cue phrases, and domain
specific knowledge to build an effective semantic lexical chaining.
3 The Corpus
In our research project we used 17 transcripts recorded from three diverse meeting
environments: industrial, organizational and educational, each involving a multi-party
conversation and containing an accurate and unedited record of the meetings and
corresponding speakers. The meeting transcripts which were varied in size, ranging
from 2,479 to 25,670 words, were multi-party conversation, and some had no pre-set
agendas. Consequently the analysis of these transcripts posed an additional complex-
ity due to their informal style, their lack of structure, their argumentative nature, and
the usage of common colloquial words. The transcripts also contain incomplete sen-
tences, sentences related to social chatting, interruptions, and references by partici-
pants made to visual context. In this paper, a corpus with a total of 247238 words is
used to illustrate our algorithm for confidentiality reasons.
Fig. 1. A Three-Stage Segmentation process.
4 Tracker Text Segmentation Algorithm (TTS)
Our TTS algorithm, which builds on the concept of sliding window, uses the utter-
ance as the base unit of analysis. The algorithm is context driven segmentation, com-
bining lexical chaining method with more semantic complex types of lexical cohesion
relationships between words in the transcripts in order to capture their sense relations,
such as synonymy, hypernymy (ISA relation), hyponymy (kind-of relation), merony-
my (part-of relation) and coordinate terms (e.g. computer and PC). Using WordNet
and our extended version of WordNet these sense cases allow us to capture the hie-
rarchical as well the transitivity relationships among the words in the transcripts and
enhance the formation of lexical chains.
The study of these transcripts has led to the identification of major speech cue phrases
used by the speakers to introduce new topics or highlighting new issues or a prob-
lems, examples of these are give in table 2. Prior to segmentation our transcripts have
been subjected to pre-processing transcripts which involve tokenisation, POS tagging
using WMATRIX, case folding, identification of compound concepts and removal of
stop words.
There are three main stages performed by TTS: (i) initial segmentation, (ii) interme-
diate, and (iii) final segmentation (shown in Fig. 1).
4.1 Initial Segmentation
This stage involves the segmentation of the stream of transcribed meetings into topi-
cally cohesive items of discussion. It is based on the sliding window approach devel-
oped by [10] and later adopted by [19], which divides the text into multi-paragraph
blocks and then using a vector space model it calculates the similarity of two con-
secutive blocks using the cosine value, a measure which has been widely used in
Information Retrieval (IR) systems. Instead of paragraphs as the core base for seg-
mentation our algorithm computes the similarity between utterances, referred to here-
by as the Utterance Cosine Similarity (UCS). Thus instead of measuring the similari-
ty between a query and a document as applied in IR systems, UCS measures the simi-
larity between two utterances.
An utterance
U is defined as
U = {
W . . .
W }, whereby,
W is a noun or com-
pound noun as it appears in the utterance. A term frequency vector
if is constructed
for each utterance
U by recording its frequency of occurrence within the transcript.
Let us suppose a transcript consists of 33 distinct noun concepts, and one of its utter-
ances is
U which includes the four distinct concept nouns: size, board, laptop, and
edge, its frequency vector representation will be denoted as follows.
= {1, 3, 1, 1, 0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0, 0,0,0,0,0,0,0,0,0,0,0,0,0}
In order to identify the similarity (sim) between two utterances,
U and
, the
cosine of their frequency vectors should be close or equal to 1. The UCS measure,
denoted sim (
U ,
U ), is defined as follows:
U ,
U )= cos( fi , jf ) =
where 0
cos ( if , jf ) 1.
is the inner product of if and jf , which measures how much the two
vectors have in common.
ff )()(
is a product of the two vector
lengths which is used to normalise the vectors.
The cosine similarity measure assumes that similar terms tend to occur in similar
segments. In such instances, the angle between them will be small, and so the cosine
similarity measure will be close to 1. Utterances with little in common will have dis-
similar terms, the calculated angle between them will be close to π/2 and the UCS
measure will be close to zero. A UCS matrix can then be prepared based on the com-
parison of each utterance with every other utterance in the transcript. An example of
this matrix is shown in Fig. 2. The blank lines in Fig. 2 contain zero vectors; these
zeros are removed for clarity. In our study after experimentation with our corpus the
threshold value was set to five.
Fig. 2. A typical UCS Matrix.
Fig. 3. Distribution of Lexical Chains within Transcript 120902TR.
4.2 Intermediate Segmentation
This stage builds the lexical chains which are generated through selected features and
grouped based on their semantic senses relations as they appear in the transcript.
Details of the algorithm to generate these chains are found in [5]. The frequency of
each chain is examined based on the occurrences of each chain member in the win-
dow (Fig. 3). The highest frequency lexical chain is then identified and is used to
extend the window or slide the window following the distribution of the topic chain
members in the transcript (Fig. 4). As the window expands, it will reach a stage whe-
reby the appearance of any of the members from that particular lexical chain fades
away. This is the point where the intermediate
topic boundary is identified. This step
Fig. 4. Sliding Window Effect.
is based on the algorithm of [15] who states that ‘a high concentration of chain-begin
and end points between the two adjacent textual units is a good indication of a boun-
dary point between two distinct news stories’.
4.3 Final Segmentation
The final segmentation refines further the new segments by searching for any speech
cue phrases to confirm the topic boundary or re-examine the boundary of this seg-
ment. Unlike the domain independent cues used by [11] and the domain specific cues
used by [19], our speech cue phrases were manually extracted from the corpus. An
example of these cues is shown in Table 2.
Table 2. Speech Cue Phrases Extracted from our Corpus.
5 Evaluation and Results
The segmentation was evaluated by comparing TTS against the TextTiling and C99
methods. Three types of evaluation metrics were used, the
P [1],
P , and
WindowDiff [17]. The results were very encouraging and showed that TTS has
outperformed both algorithms (Fig. 5).
TextTiling was the most underperforming algorithm for this corpus, possibly due to
1. its lexical cohesion-based algorithm which depends mainly on repetition.
There are many cases in our transcripts where few consecutive utterances in-
cludes no word repetitions and consequently TextTiling identified them as
four different topics;
2. its dependence on sentence-based structure and not utterance-based struc-
ture. The similarity measure used in TextTiling compares pair of sentences,
and consequently relevant to structured and well punctuated texts but unsuit-
able for our ill-structured corpus;
3. the unsuitability of using a fixed window size.
Fig. 5. Evaluation of TTS.
6 Conclusions
The TTS algorithm described in this paper is an iterative process that offers a great
potential for analysing transcribed meetings involving a multi-party conversation. The
study has extended the use of cosine similarity measure to transcribed texts and im-
proved the performance of lexical chaining methods and text segmentation algorithms
by including complex semantic relations and speech specific cue phrases.
Although the evaluation results highlighted the effectiveness of TTS compared to
TextTiling and C99, there are few limitations related to the issue of compound words
and the POS tagging system used. The identification algorithm of compound words
developed in this study, has given, in some situations, unsatisfactory results, as not all
the compound words were the result of combined nouns. Also some compound words
in the corpus such as ‘high voltage line’ and ‘natural language processing’ were not
automatically identified, partly due to the limitation of WMATRIX. Future work will
attempt to resolve these problems.
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