Learning Taxonomy for Text Segmentation by Formal Concept Analysis

TL;DR

This paper introduces a novel text segmentation method using Formal Concept Analysis to derive a taxonomy and cluster sentences based on concepts, providing a conceptual framework for improved text segmentation.

Contribution

It presents the Concept-oriented Clustering Segmentation (COCS) algorithm that applies FCA and k-means clustering for concept-driven text segmentation.

Findings

Experimental results demonstrate the effectiveness of COCS

The method offers a conceptual view for text segmentation

Clustering improves segmentation accuracy

Abstract

In this paper the problems of deriving a taxonomy from a text and concept-oriented text segmentation are approached. Formal Concept Analysis (FCA) method is applied to solve both of these linguistic problems. The proposed segmentation method offers a conceptual view for text segmentation, using a context-driven clustering of sentences. The Concept-oriented Clustering Segmentation algorithm (COCS) is based on k-means linear clustering of the sentences. Experimental results obtained using COCS algorithm are presented.