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Relation extraction is the task of extracting semantic relations between entities in a sentence. It is an essential part of some natural language processing tasks such as information extraction, knowledge extraction, question answering, and knowledge base population. The main motivations of this research stem from a lack of a dataset for relation extraction in the Persian language as well as the necessity of extracting knowledge from the growing big data in the Persian language for different applications. In this paper, we present "PERLEX" as the first Persian dataset for relation extraction, which is an expert-translated version of the "SemEval-2010-Task-8" dataset. Moreover, this paper addresses Persian relation extraction utilizing state-of-the-art language-agnostic algorithms. We employ six different models for relation extraction on the proposed bilingual dataset, including a non-neural model (as the baseline), three neural models, and two deep learning models fed by multilingual BERT contextual word representations. The experiments result in the maximum F1-score of 77.66% (provided by BERTEM-MTB method) as the state of the art of relation extraction in the Persian language. [ABSTRACT FROM AUTHOR]

Feature selection is an important preprocessing task in classification that eliminates the irrelevant, redundant, and noisy features. Improving the performance of model, decreasing the computational cost, and adjusting the “curse of dimensionality” are the key advantages of feature selection task. The evolution process of the existing multi-objective based feature selection algorithms is relied on the objective space while the problem space contains useful information. This paper proposes a multi-objective PSO based method named RFPSOFS that ranks the features based on their frequencies in the archive set. Then, these ranks are used to refine the archive set and guide the particles. The proposed method is compared with three PSO based and one genetic based multi-objective methods on 9 Benchmark datasets. Qualitative and quantitative analyses of the results are performed by visual analysis of the Pareto fronts and three performance metrics respectively. Finally, remarkable performance in datasets with more than hundred features and satisfactory performance in datasets with less than hundred features are obtained. [ABSTRACT FROM AUTHOR]