Using virtual edges to extract keywords from texts modeled as complex
networks
- URL: http://arxiv.org/abs/2205.02172v1
- Date: Wed, 4 May 2022 16:43:03 GMT
- Title: Using virtual edges to extract keywords from texts modeled as complex
networks
- Authors: Jorge A. V. Tohalino and Thiago C. Silva and Diego R. Amancio
- Abstract summary: We modeled texts co-occurrence networks, where nodes are words and edges are established by contextual or semantical similarity.
We found that, in fact, the use of virtual edges can improve the discriminability of co-occurrence networks.
- Score: 0.1611401281366893
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Detecting keywords in texts is important for many text mining applications.
Graph-based methods have been commonly used to automatically find the key
concepts in texts, however, relevant information provided by embeddings has not
been widely used to enrich the graph structure. Here we modeled texts
co-occurrence networks, where nodes are words and edges are established either
by contextual or semantical similarity. We compared two embedding approaches --
Word2vec and BERT -- to check whether edges created via word embeddings can
improve the quality of the keyword extraction method. We found that, in fact,
the use of virtual edges can improve the discriminability of co-occurrence
networks. The best performance was obtained when we considered low percentages
of addition of virtual (embedding) edges. A comparative analysis of structural
and dynamical network metrics revealed the degree, PageRank, and accessibility
are the metrics displaying the best performance in the model enriched with
virtual edges.
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