首页|Researchers from Nanjing University of Science and Technology Detail New Studies and Findings in the Area of Machine Learning (Exploring Motivations for Algorit hm Mention In the Domain of Natural Language Processing: a Deep Learning Approac h)
Researchers from Nanjing University of Science and Technology Detail New Studies and Findings in the Area of Machine Learning (Exploring Motivations for Algorit hm Mention In the Domain of Natural Language Processing: a Deep Learning Approac h)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews-A new study on Machine Learning is now available. According to news reporting originatingin Nanjing, People's Republic of China, by NewsRx journalists, research stated, "With the formation ofthe fourth parad igm of scientific research, algorithms have become increasingly important in sci entificresearch. In academic papers, algorithms may be mentioned by scholars wi th various motivations, using,comparing, or improving algorithms to solve compl ex research tasks."Financial support for this research came from National Natural Science Foundatio n of China (NSFC).The news reporters obtained a quote from the research from the Nanjing Universit y of Science andTechnology, "Identifying these motivations can help scholars di scover the relationships between algorithmsand further assess their roles and v alues. Therefore, taking the field of natural language processing(NLP) as an ex ample, this article proposes a complete method to conduct the identification, di stribution,and evolution of motivations for mentioning algorithms at the senten ce level. Specifically, using manual annotation and machine learning methods, we identify algorithm entities and sentences in the full textof papers, classify motivations for mentioning algorithms by pre-training models and data augmentati ontechniques, and finally analyze the distribution and evolution of motivations . The results show that thedeep learning models trained with the augmented data outperform the traditional machine learning modelsin the classification task. In academic papers, more than half of the sentences show the direct use ofalgor ithms, while the lowest percentage of motivations are improving algorithms, and the diversity ofmotivations has been increasing with time. For specific algorit hms, grammatical algorithms are mentionedmore by the motivation of ‘description ,' while more motivations of ‘use' are found in the machine learningalgorithms category. As time passed, the ‘use' motivations gradually replaced the ‘descript ion' motivationsfor different algorithms, and the number of motivation types de creased significantly."
NanjingPeople's Republic of ChinaAsi aAlgorithmsCyborgsEmerging TechnologiesMachine LearningNatural Languag e ProcessingNanjing University of Science and Technology
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