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  5. A Word Embedding Transfer Model for Robust Text Categorization

A Word Embedding Transfer Model for Robust Text Categorization

来源 :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) | 被引量 : 0次 | 上传用户：waly7208346
【摘 要】
：
It is common to fine-tune pre-trained word embeddings in text categorization.However,we find that fine-tuning does not guarantee improvement across text categorization datasets,while could introduce c
【作 者】
：
Yiming Zhang Jing Wang Weijian Deng Yaojie Lu 
【机 构】
：
Institute of Information Engineering,CAS Beijing
【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)
【发表日期】
：
2018年9期
【关键词】
：
Word Embedding Text Categorization Transfer Learning 
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　　It is common to fine-tune pre-trained word embeddings in text categorization.However,we find that fine-tuning does not guarantee improvement across text categorization datasets,while could introduce considerable parameters to model.In this paper,we study new transfer methods to solve the problems above,and propose “Robustness of OOVs” to provide a perspective to reduce memory consumption further.The experimental results show that the proposed method is proved to be a good alternative to fine-tuning method on large dataset.
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