Sentence Classification and Information Retrieval for Petroleum Engineering - Ferraz, Thiago; Ferreira, Gabriel; Cozman, Fábio; Santos, Ismael (Anais do Encontro Nacional de Inteligência Artificial e Computacional (ENIAC); 2019: Anais do XVI Encontro Nacional de Inteligência Artificial e Computacional, 2019)
Classifying sentences in industrial, technical or scientific reports can enhance text mining and information retrieval tasks with useful machinereadable metadata. This paper describes a search engine that employs sentence classification so as to search for abstracts from scholarly papers in Petroleum Engineering. The sentences were classified into four classes, based on the popular IMRAD categories. We produced a dataset containing more than 2,200 manually labeled sentences from 278 scholarly articles in the field of Petroleum Engineering in order to be used as training and testing data. The classifier with best results was logistic regression, with an accuracy of 86.4%. The information retrieval system built on top of the classification system yielded a mAP of 0.80.