Lifelong Learning CRF for Supervised Aspect Extraction
This addresses the problem of domain adaptation in aspect extraction for natural language processing applications, though it appears incremental as it builds on existing CRF methods.
This paper tackles the problem of supervised aspect extraction by demonstrating that Conditional Random Fields (CRF) can leverage knowledge from past domains in a lifelong learning manner, resulting in markedly better extraction in new domains compared to traditional CRF without prior knowledge.
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.