  Text segmentation (TS) aims at dividing long text into coherent segments
which reflect the subtopic structure of the text. It is beneficial to many
natural language processing tasks, such as Information Retrieval (IR) and
document summarisation. Current approaches to text segmentation are similar in
that they all use word-frequency metrics to measure the similarity between two
regions of text, so that a document is segmented based on the lexical cohesion
between its words. Various NLP tasks are now moving towards the semantic web
and ontologies, such as ontology-based IR systems, to capture the
conceptualizations associated with user needs and contents. Text segmentation
based on lexical cohesion between words is hence not sufficient anymore for
such tasks. This paper proposes OntoSeg, a novel approach to text segmentation
based on the ontological similarity between text blocks. The proposed method
uses ontological similarity to explore conceptual relations between text
segments and a Hierarchical Agglomerative Clustering (HAC) algorithm to
represent the text as a tree-like hierarchy that is conceptually structured.
The rich structure of the created tree further allows the segmentation of text
in a linear fashion at various levels of granularity. The proposed method was
evaluated on a wellknown dataset, and the results show that using ontological
similarity in text segmentation is very promising. Also we enhance the proposed
method by combining ontological similarity with lexical similarity and the
results show an enhancement of the segmentation quality.
