Lifelong Learning CRF for Supervised Aspect Extraction
Lifelong Learning CRF for Supervised Aspect Extraction
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DOI：
10.18653/v1/p17-2023
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发表时间：
2017-04
期刊：
影响因子：
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通讯作者：
Lei Shu;Hu Xu;B. Liu
中科院分区：
文献类型：
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作者：
Lei Shu;Hu Xu;B. Liu
本文对有监督的体抽取做了重点研究。它表明，如果系统已经从许多过去的领域进行方面提取，并保留他们的结果作为知识，条件随机场（CRF）可以利用这些知识在终身学习的方式提取在一个新的领域显着优于传统的CRF，而不使用这种先验知识。关键的创新在于，即使在CRF训练之后，该模型仍然可以通过应用中的经验来改进其提取。
This paper makes a focused contribution to supervised aspect extraction. It shows that if the system has performed aspect extraction from many past domains and retained their results as knowledge, Conditional Random Fields (CRF) can leverage this knowledge in a lifelong learning manner to extract in a new domain markedly better than the traditional CRF without using this prior knowledge. The key innovation is that even after CRF training, the model can still improve its extraction with experiences in its applications.