Open AccessACERPI: An approach for ordinances collection, information extraction and entity resolution
Author(s) -
Christian Schmitz,
Serigne K. Mbaye,
Edimar Mânica,
Renata Galante
Publication year - 2021
Language(s) - English
Resource type - Conference proceedings
DOI - 10.5753/sbbd.2021.17869
Subject(s) - computer science , information retrieval , information extraction , resolution (logic) , filter (signal processing) , recall , precision and recall , measure (data warehouse) , world wide web , data mining , artificial intelligence , linguistics , philosophy , computer vision
Ordinances are documents issued by federal institutions that contain, among others, information regarding their staff. These documents are accessible through public repositories that usually do not allow any filter or advanced search on documents’ contents. This paper presents ACERPI, an approach which identifies the people mentioned in the ordinances to help the user find the documents of interest. ACERPI combines techniques to discover, obtain, convert and structure documents, extract information, and link employees entities. Experiments were performed on two real datasets and demonstrated a recall of 72.7% for our named entity recognition model trained with only 534 samples and F1 measure of 90% in the efficacy of the entity resolution technique.