OntoSeg: a Novel Approach to Text Segmentation using Ontological Similarity

TL;DR

OntoSeg introduces an ontological similarity-based method for text segmentation, leveraging conceptual relations and hierarchical clustering to improve segmentation quality over traditional lexical approaches.

Contribution

The paper presents a novel ontological similarity measure for text segmentation, integrating it with hierarchical clustering to capture conceptual relations and enable multi-granularity segmentation.

Findings

OntoSeg outperforms traditional lexical cohesion methods on standard datasets.

Combining ontological and lexical similarities enhances segmentation accuracy.

The hierarchical structure allows flexible segmentation at different levels of detail.

Abstract

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.