Learning Structured Representations of Entity Names using Active Learning and Weak Supervision
This addresses entity normalization and variant generation tasks, offering a solution with minimal labeled data, though it appears incremental as it combines existing techniques.
The paper tackles the problem of learning structured representations of entity names without context or external knowledge, achieving high-quality models from only about a dozen labeled examples.
Structured representations of entity names are useful for many entity-related tasks such as entity normalization and variant generation. Learning the implicit structured representations of entity names without context and external knowledge is particularly challenging. In this paper, we present a novel learning framework that combines active learning and weak supervision to solve this problem. Our experimental evaluation show that this framework enables the learning of high-quality models from merely a dozen or so labeled examples.