Development of a Python tool for graph based representation of multiple texts
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Date
2024-07-10
Authors
Ασημακόπουλος, Διονύσιος
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Abstract
In this thesis, we present the construction of a Python code, a tool for representing an entire
corpus of documents as a single graph, as proposed by Giarelis et al. (2020). This model enables
the representation and operation of a wide variety of graph-based algorithms in fields such as
natural language processing, along with a bibliographical study of the graph theory used and the
evolution of the field from earlier techniques of information retrieval (IR) to the graph approaches
prevalent today.
Our tool introduces a novel approach, describing and creating a single graph that
incorporates all the documents from a given set. This capability allows operations on the entire set
with functionalities that were previously unattainable due to limitations in earlier approaches, such
as the lack of metrics between different documents and the absence of a structural framework to
analyze words, sentences, and documents as a cohesive unit.
It is implemented in Python 3.9 and utilizes other libraries such as NetworkX (Hagberg,
Swart & Chult, 2008), offering flexibility in creating the appropriate graph-of-docs. Chapter 2
presents the theoretical background of graph theory and state-of-the-art approaches, while Chapter
3 demonstrates use cases, providing an explanation of the code as an implementation of the graph-of-docs representation model. Finally, chapter 4 concludes our work and proposes future
directions.
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Keywords
Graph-of-docs, Natural language processing, Graph-based algorithms, NetworkX