A New Entity Extraction Model Based on Journalistic Brazilian Portuguese Language to Enhance Named Entity Recognition

Named Entity Recognition (NER) plays an important role on broad natural language processing applicability. According to the literature, the NER process applied to the English language reaches around 90% of accuracy. However, when applied to Portuguese, this accuracy is at most 83.38%. A wide range of algorithms based on LSTM (Long-Short Term Memory) architecture has being proposed to enhance the NER accuracy. However, a key component to a successful information extraction is the corpora used for NER training. In order to improve the NER in Portuguese language, this paper proposes a methodology for training text corpus based on Portuguese-language journalistic corpora. The Journalistic language has the best adherence to the contemporaneity of the language, since it preserves features such as objectivity, simplicity, impartiality, and is a reference of transmitting the information without ambiguity. The proposed methodology provides a model to extract entities and assess the obtained results with the use of Recurrent Neural Network architectures. At the best of our knowledge, with the proposed methodology, the NER task applied to the Portuguese language overcomes the average accuracy found in the literature, increased from 83.38% to 85.64%. Moreover, the use of this methodology could decrease the computational costs related to the NER processing tasks.