Lifelong Learning CRF for Supervised Aspect Extraction

TL;DR

This paper introduces a lifelong learning approach for supervised aspect extraction using CRF models, enabling them to improve over time by leveraging past domain knowledge, resulting in better extraction performance in new domains.

Contribution

It presents a novel lifelong learning method for CRF-based aspect extraction that incorporates past domain knowledge to enhance future extraction accuracy.

Findings

CRF models with lifelong learning outperform traditional CRF in new domains.

The approach effectively leverages past domain data to improve aspect extraction.

Experiments show significant performance gains over baseline methods.

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.