mlsanityData to 14 Oct 2026

Lifelong Learning CRF for Supervised Aspect Extraction

arXiv 1705.00251 ↗First posted 29 Apr 2017cs.CLcited by 9

Not on the map. Why?

Abstract (arXiv)

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.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Apr 2017.

Builds on

No reference list yet.

Built on by

Cited by year

Citing papers per year: 2021: 6; 2022: 3.

9 papers.

  1. 2021Achieving Forgetting Prevention and Knowledge Transfer in Continual Learningcited by 91
  2. 2021Adapting BERT for Continual Learning of a Sequence of Aspect Sentiment Classification Taskscited by 46
  3. 2021CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Taskscited by 29
  4. 2021Deep Bayesian Unsupervised Lifelong Learningcited by 21
  5. 2021Continual Learning with Knowledge Transfer for Sentiment Classificationcited by 12
  6. 2022Challenges for Open-domain Targeted Sentiment Analysiscited by 9
  7. 2022Zero-Shot Aspect-Based Sentiment Analysiscited by 5
  8. 2022Syntax-Guided Domain Adaptation for Aspect-based Sentiment Analysiscited by 1
  9. 2021Recommend for a Reason: Unlocking the Power of Unsupervised Aspect-Sentiment Co-Extractioncited by 0