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The identification of indirect relationships between texts from different sources makes the task of text mining useful when the goal is to obtain the most valuable information from a set of texts. That is why in the field of information retrieval the correct recognition of named entities plays an important role when extracting valuable information in large amounts of text. Therefore, it is important to propose techniques that improve the NER classifiers in order to achieve the correct recognition of named entities. In this work, a graph structure for storage and enrichment of named entities is proposed. It makes use of synonyms and domain-specific ontologies in the area of computing. The performance of the proposed structure is measured and compared with other NER classifiers in the experiments carried out.
Muñoz, David et al. “Named Entity Recognition based on a Graph Structure.” POLIBITS 60 (2019): 35-41. DOI: 10.13053/cys-24-2-3379