Semantic flow in language networks

In this study we propose a framework to characterize documents based on their semantic flow. The proposed framework encompasses a network-based model that connected sentences based on their semantic similarity. Semantic fields are detected using standard community detection methods. as the story unf...

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Bibliographic Details
Published in:arXiv.org 2019-05
Main Authors: Corrêa, Edilson A, Marinho, Vanessa Q, Amancio, Diego R
Format: Article
Language:English
Subjects:
Graph theory
Markov processes
Optimization
Semantics
Sentences
Texts
Transportation networks
Online Access:Get full text
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Summary:In this study we propose a framework to characterize documents based on their semantic flow. The proposed framework encompasses a network-based model that connected sentences based on their semantic similarity. Semantic fields are detected using standard community detection methods. as the story unfolds, transitions between semantic fields are represent in Markov networks, which in turned are characterized via network motifs (subgraphs). Here we show that the proposed framework can be used to classify books according to their style and publication dates. Remarkably, even without a systematic optimization of parameters, philosophy and investigative books were discriminated with an accuracy rate of 92.5%. Because this model captures semantic features of texts, it could be used as an additional feature in traditional network-based models of texts that capture only syntactical/stylistic information, as it is the case of word adjacency (co-occurrence) networks.
ISSN:2331-8422
DOI:10.48550/arxiv.1905.07595