The Relation Extraction (RE) is an important basic Natural Language Processing (NLP) for many applications, such as search engines, recommender systems, question-answering systems and others. There are many studies in this subarea of NLP that continue to be explored, such as SemEval campaigns (2010 to 2018), or DDI Extraction (2013).For more than ten years, different RE systems using mainly statistical models have been proposed as well as the frameworks to develop them. This paper focuses on frameworks allowing to develop such RE systems using deep learning models. Such frameworks should make it possible to reproduce experiments of various deep learning models and pre-processing techniques proposed in various publications. Currently, there are very few frameworks of this type, and we propose a new open and optimizable framework, called DeepREF, which is inspired by the OpenNRE and REflex existing frameworks. DeepREF allows the employment of various deep learning models, to optimize their use, to identify the best inputs and to get better results with each data set for RE and compare with other experiments, making ablation studies possible. The DeepREF Framework is evaluated on several reference corpora from various application domains.

This report presents the results of the shared tasks organized as part of the VarDial Evaluation Campaign 2022. The campaign is part of the ninth workshop on Natural Language Processing (NLP) for Similar Languages, Varieties and Dialects (VarDial), co-located with COLING 2022. Three separate shared tasks were included this year: Identification of Languages and Dialects of Italy (ITDI), French Cross-Domain Dialect Identification (FDI), and Dialectal Extractive Question Answering (DialQA). All three tasks were organized for the first time this year.

This article presents the model that generated the runs submitted by the R2I_LIS team to the VarDial2019 evaluation campaign, more particularly, to the binary classification by dialect sub-task of the Moldavian vs. Romanian Cross-dialect Topic identification (MRC) task. The team proposed a majority vote-based model, between five supervised machine learning models, trained on forty manually-crafted features. One of the three submitted runs was ranked second at the binary classification sub-task, with a performance of 0.7963, in terms of macro-F1 measure. The other two runs were ranked third and fourth, respectively.