汉语中的被动句根据有无被动标记词可分为有标记被动句和无标记被动句。由于其形态构成复杂多样,给自然语言理解带来很大困难,因此实现汉语被动句的自动识别对自然语言处理下游任务具有重要意义。该文构建了一个被动句语料库,提出了一个融合词性和动词论元框架信息的PC-BERT-CNN模型,对汉语被动句进行自动识别。实验结果表明,该文提出的模型能够准确地识别汉语被动句,其中有标记被动句识别F1值达到98.77%,无标记被动句识别F1值达到96.72%。
Abstract
Chinese passive sentences can be classified into marked and unmarked passive sentences based on the presence of passive markers. Due to their complex and diverse forms, they pose significant challenges to natural language understanding. Therefore, the automatic recognition of Chinese passive sentences is important for downstream tasks in natural language processing. In this paper, we construct a corpus specifically for passive sentences and propose a PC-BERT-CNN model that integrates part-of-speech and verb argument frame information to automatic Chinese passive sentence identification. Experiment results demonstrate the proposed model achieves 98.77% F1 score for marked passive sentence recognition, and 96.72% for unmarked passive sentence recognition.
关键词
汉语被动句 /
自动识别 /
特征融合 /
语料库
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Key words
Chinese passive sentences /
automatic recognition /
feature fusion /
corpus
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参考文献
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脚注
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基金
国家社会科学基金(21&ZD288)
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