ArabRelat: Arabic Relation Extraction using Distant Supervision

Mohamed, Reham Hussein; El-Makky, Nagwa M.; Nagi, Khaled

doi:10.5220/0005636604100417

Cited by 6 publications

(2 citation statements)

References 8 publications

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“…In these experiments, we heuristically measure the impact of reducing the number of negative relation instances on the models' accuracy by reducing or removing the relation instances in the documents that are not mentioned in the distant supervision sources. We also explicitly add some negative relation instances in the training datasets of one relation class in order to decrease in the true positive rate while maintaining a low false positive rate as recommended by Mohamed et al [40]. Table 7 above shows the impact of reducing the number of negative Relation Instances on ML models' accuracy in terms of Precision, Recall and F1-measure.…”

Section: A Configuring the Training Datasetsmentioning

confidence: 99%

Smart Information Retrieval: Domain Knowledge Centric Optimization Approach

et al. 2019

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In the age of the Internet of Things, online data have witnessed a significant growth in terms of volume and diversity, and research into information retrieval has become one of the important research themes in the Internet-oriented data science research. This paper introduces a novel domain knowledge centric methodology aimed at improving the accuracy of using machine learning methods for relation extraction from text data, which is critical to the accuracy and efficiency of information retrievalbased applications, including recommender systems and sentiment analysis. The proposed methodology makes a significant contribution to the processes of domain knowledge-based relation extraction including interrogating Linked Open Datasets to generate the relation classification training data, addressing the imbalanced classification in the training datasets, determining the probability threshold of the best learning algorithm, and establishing the optimum parameters for genetic algorithms, which were utilized to optimize the feature selection for the learning algorithms. The experimental evaluation of the proposed methodology reveals that the adopted machine-learning algorithms exhibit higher precision and recall in relation extraction in the reduced feature space optimized by our implementation. The considered machine learning includes support vector machine, perceptron algorithm uneven margin, and K-nearest neighbors. The outcome is verified by comparing against the random mutation hill-climbing optimization algorithm using Wilcoxon signed-rank statistical analysis.

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Section: A Configuring the Training Datasetsmentioning

confidence: 99%

Smart Information Retrieval: Domain Knowledge Centric Optimization Approach

et al. 2019

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“…In the past few years, more and more scholars have begun to focus on techno-stress. Existing research from different perspectives has discussed the generation of technical pressure (why individuals perceive the pressure of technology) [1,2], the causes of technical stress [3,4] and its results [5,6,7,8], as well as the way in which techno-stress addressed [9].…”

Section: Introductionmentioning

confidence: 99%

Techno-Stress of Teachers: An Empirical Investigation from China

Yang¹,

Yang²,

Yuan³

et al. 2017

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Abstract. The purpose of this study is to explore the consequences and interrelations among techno-stressors, techno-and work-exhaustion in Chinese teachers. For this purpose, an empirical study with 272 teachers in China was carried out. Results indicate that techno-induced role ambiguity and techno-change frequency have a direct impact on techno-exhaustion. Our results also reveal that both work-exhaustion and techno-exhaustion negatively impact job satisfaction and teacher retention; meanwhile, techno-exhaustion indirectly causes work-exhaustion. Then discussions of Techno-stress management strategies were discussed in this paper.

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