A probabilistic segment model combining lexical cohesion and disruption for topic segmentation (Un mod\`ele segmental probabiliste combinant coh\'esion lexicale et rupture lexicale pour la segmentation th\'ematique) [in French]

Leveraging Lexical Cohesion and Disruption for Topic Segmentation

OntoSeg: a Novel Approach to Text Segmentation using Ontological Similarity

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) an…

BeamSeg: A Joint Model for Multi-Document Segmentation and Topic Identification

We propose BeamSeg, a joint model for segmentation and topic identification of documents from the same domain. The model assumes that lexical cohesion can be observed across documents, meaning that segments describing th…

Semantic Feature Structure Extraction From Documents Based on Extended Lexical Chains

The meaning of a sentence in a document is more easily determined if its constituent words exhibit cohesion with respect to their individual semantics. This paper explores the degree of cohesion among a document’s words …

Hrebs and Cohesion Chains as similar tools for semantic text properties research

In this study it is proven that the Hrebs used in Denotation analysis of texts and Cohesion Chains (defined as a fusion between Lexical Chains and Coreference Chains) represent similar linguistic tools. This result gives…