Lifelong Learning CRF for Supervised Aspect Extraction

Lei Shu, Hu Xu, Bing Liu


Abstract
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.
Anthology ID:
P17-2023
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
148–154
Language:
URL:
https://www.aclweb.org/anthology/P17-2023
DOI:
10.18653/v1/P17-2023
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/P17-2023.pdf
Video:
 https://vimeo.com/234956125