Abstract

Given a text, can we segment it into semantically coherent sections in an automatic way? Can we detect the semantic boundaries, if we know how many they are? Can we determine how many semantically distinct sections are in the text? These are the questions we address in this paper. To respond, we use the Bidirectional Encoder Representation from Transformer (BERT) to analyze the text and evaluate a function that we call local incoherence, which we expect to show maxima at the points where a semantic boundary is detected. Our results, although preliminary, are encouraging and suggest that our approach can be successfully applied. However, they are quite sensitive with respect to the text quality, as it happens in the case in which the text is derived from an audio stream via Automatic Speech Recognition techniques.

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Cite this article

@article{cai-ADSI-8,
  author  = {Tiziano Lattisi and Davide Farina and Marco Ronchetti},
  title   = {Semantic Segmentation of Text Using Deep Learning},
  journal = {Computing and Informatics},
  volume  = {41},
  number  = {1},
  pages   = {78--97},
  year    = {2022},
  issn    = {1335-9150},
  doi     = {10.31577/cai_2022_1_78}
}
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