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Definition Extraction with Balanced Random Forests

机译:平衡随机森林的定义提取

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摘要

We propose a novel machine learning approach to the task of identifying definitions in Polish documents. Specifics of the problem domain and characteristics of the available dataset have been taken into consideration, by carefully choosing and adapting a classification method to highly imbalanced and noisy data. We evaluate the performance of a Random Forest-based classifier in extracting definitional sentences from natural language text and give a comparison with previous work.
机译:我们提出了一种新颖的机器学习方法来识别波兰文档中的定义。通过仔细选择分类方法并使其适应高度不平衡且嘈杂的数据,已考虑到问题域的细节和可用数据集的特征。我们评估基于随机森林的分类器从自然语言文本中提取定义句子的性能,并与以前的工作进行比较。

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