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A Context-Aware Approach for the Identification of Complex Words in Natural Language Texts

机译：一种语境意识到自然语言文本中复杂词的识别方法

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This paper evaluates the effect of the context on the identification of complex words in natural language texts. The approach automatically tags words as either complex or not, based on two sets of features: base features that only pertain to the target word, and contextual features that take the context of the target word into account. We experimented with several supervised machine learning models, and trained and tested the approach with the SemEval-2016 dataset. Results show that considering contextual features significantly improves the identification of complex words by reaching an F-measure of 0.260 compared to 0.184 without them.
机译：本文评估了上下文对自然语言文本中复杂词的识别的影响。该方法基于两组特征：基本功能仅适用于目标字的基本功能以及将目标字的上下文考虑的基本功能标记为复杂的单词。我们尝试了多种监督机器学习模型，并通过Semeval-2016数据集进行了培训并测试了方法。结果表明，考虑上下文特征显着提高了复杂单词的识别，通过达到0.260的F-Measure，而不是它们的0.184。

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