KCAT: A Knowledge-Constraint Typing Annotation Tool
This addresses the problem of noisy and inefficient annotation for fine-grained entity typing, offering a domain-specific tool to enhance crowdsourcing workflows.
The paper tackles the challenge of fine-grained entity typing by introducing KCAT, a tool that reduces candidate types via entity linking and uses a multi-step typing scheme to improve annotation efficiency, showing that time consumption increases slowly with type set size.
Fine-grained Entity Typing is a tough task which suffers from noise samples extracted from distant supervision. Thousands of manually annotated samples can achieve greater performance than millions of samples generated by the previous distant supervision method. Whereas, it's hard for human beings to differentiate and memorize thousands of types, thus making large-scale human labeling hardly possible. In this paper, we introduce a Knowledge-Constraint Typing Annotation Tool (KCAT), which is efficient for fine-grained entity typing annotation. KCAT reduces the size of candidate types to an acceptable range for human beings through entity linking and provides a Multi-step Typing scheme to revise the entity linking result. Moreover, KCAT provides an efficient Annotator Client to accelerate the annotation process and a comprehensive Manager Module to analyse crowdsourcing annotations. Experiment shows that KCAT can significantly improve annotation efficiency, the time consumption increases slowly as the size of type set expands.