Name
Abstract | ||
|---|---|---|
Human experts can often easily write a set of approximate rules based on their domain knowledge for supporting automatic text classification. While such approximate rules are able to conduct classification at a general level, they are not effective for handling diverse and specific situations for a particular category. Given a set of approximate rules and a moderate amount of labeled data, existing incremental text classification learning models can be employed for tackling this problem by continuous rule refinement. However, these models lack the consideration of context information, which inherently exists in data. We propose a framework comprising rule embeddings and context embeddings derived from data to enhance the adaptability of approximate rules via considering the context information. We conduct extensive experiments and the results demonstrate that our proposed framework performs better than existing models in some benchmarking datasets, indicating that learning the context of rules is constructive for improving text classification performance. |
Year | Venue | Field |
|---|---|---|
2018 | AIRS | Adaptability,Data mining,Domain knowledge,Constructive,Computer science,A moderate amount,Learning models,Labeled data,Benchmarking |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
7 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Wai Chung Wong | 1 | 0 | 0.34 |
| Sunny Lai | 2 | 0 | 0.68 |
| Wai Lam | 3 | 1498 | 145.11 |
| Kwong-Sak Leung | 4 | 1887 | 205.58 |