Academic Publications

@conference{ChifuLREC2022,

title = {DeepREF: A Framework for Optimized Deep Learning-based Relation Classification},

author = {Igor Nascimento and Rinaldo Lima and Adrian Chifu and Bernard Espinasse and Sébastien Fournier},

url = {http://www.lrec-conf.org/proceedings/lrec2022/pdf/2022.lrec-1.480.pdf},

year  = {2022},

date = {2022-06-20},

urldate = {2022-06-20},

booktitle = {Proceedings of the 13th Conference on Language Resources and Evaluation (LREC 2022)},

pages = {4513–4522},

publisher = {European Language Resources Association (ELRA)},

address = {Marseille, France},

abstract = {Relation Extraction (RE) is an important basic Natural Language Processing (NLP) task for many applications, including search engines and question-answering systems. There are many studies in this subarea of NLP that continue to be explored, such as the ones concerned by SemEval shared tasks. For many years, several RE systems based on statistical models have been proposed, as well as the frameworks to develop them. We focus on frameworks allowing to develop such RE systems using deep learning models. Such frameworks make it possible to reproduce experiments using many deep learning models and preprocessing techniques. Currently, there are very few frameworks of this type. In this paper, we propose an open and optimizable framework called DeepREF, inspired by two other existing frameworks: OpenNRE and REflex. DeepREF allows the rapid development of deep learning models for Relation Classification (RC). In addition, it enables hyperparameter optimization, and the application of many preprocessing techniques on the input textual data. DeepREF provides means to boost the process of running deep learning models for RC tasks on different datasets and models. DeepREF is evaluated on three reference corpora and has demonstrated competitive results compared to other state-of-the-art RC systems.},

keywords = {DDI, DeepREF, Embeddings, Framework, NLP, Optuna, Relation Classification, SemEval},

pubstate = {published},

tppubtype = {conference}

}