Using word embedding to detect keywords in texts modeled as complex networks.

Detecting keywords in texts is a task of paramount importance for many text mining applications. Graph-based techniques have been commonly used to automatically find the key concepts in texts. However, the integration of valuable information provided by embeddings to enrich the graph structure has not been widely used. In this context, this paper aims to address the following question: can the quality of extracted keywords from a co-occurrence network be enhanced by integrating embeddings to enrich the network structure? In the adopted model, texts are represented as co-occurrence networks, where nodes are words and edges are established either by contextual or semantical similarity. Two embedding approaches were used: Word2vec and Bidirectional Encoder Representations from Transformers (BERT). The results indicate that using virtual edges can effectively enhance the discriminative capacity of co-occurrence networks. The best performance was achieved by incorporating a limited proportion of virtual (embedding) edges. A comparison of the structural and dynamical network metrics demonstrated that the degree, PageRank, and accessibility metrics exhibited superior performance in the proposed model. [ABSTRACT FROM AUTHOR]