【摘 要】
：
Keyphrase extraction can provide effective ways of organiz-ing scientific documents.For this task,neural-based methods usually suffer from performance unstability due to data scarcity.In this paper,we
【机 构】
：
School of Mathematical Science,Peking University
【出 处】
：
第十七届全国计算语言学学术会议暨第六届基于自然标注大数据的自然语言处理国际学术研讨会(CCL 2018)
论文部分内容阅读
Keyphrase extraction can provide effective ways of organiz-ing scientific documents.For this task,neural-based methods usually suffer from performance unstability due to data scarcity.In this paper,we adopt the pipeline two-step method including candidate extraction and keyphrase ranking,where candidate extraction is a key to influence the whole performance.In the candidate extraction step,to overcome the low-recall problem of traditional rule-based method,we propose a novel semi-supervised data augmentation method,where a neural-based tagging model and a discriminative classifier boost each other and get more confident phrases as candidates.With more reasonable candidates,keyphrase are identified with recall promoted.Experiments on SemEval 2017 Task 10 show that our model can achieve competitive results.
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