Abstract
Competitive intelligence (CI) is a relevant area of a corporation and can support the strategic business area by showing those responsible, helping decision making on how to position an organization in the market. This work uses the Bidirectional Transformer Encoding Representations (BERT) to process a sentence and its named entities and extract the parts of the sentences that represent or describe the semantic relationship between these named entities. The approach was developed for the Portuguese language, considering the financial domain and exploring deep linguistic representations without using other lexical-semantic resources. The results of the experiments show a precision of 73.5% using the Jaccard metric that measures the similarity between sentences. A second contribution of this work is the manually constructed dataset with more than 4.500 tuples (phrase, entity, entity) annotated.
Financially supported by the Brazilian National Council for Scientific and Technological Development (CAPES) and the by Portuguese Foundation for Science and Technology (FCT)under the projects CEECIND/01997/2017, UIDB/00057/2020.
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Reyes, D.D.L., Trajano, D., Manssour, I.H., Vieira, R., Bordini, R.H. (2021). Entity Relation Extraction from News Articles in Portuguese for Competitive Intelligence Based on BERT. In: Britto, A., Valdivia Delgado, K. (eds) Intelligent Systems. BRACIS 2021. Lecture Notes in Computer Science(), vol 13074. Springer, Cham. https://doi.org/10.1007/978-3-030-91699-2_31
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