# KCAT: A Knowledge-Constraint Typing Annotation Tool

## TL;DR

KCAT is an annotation tool designed to improve the efficiency of fine-grained entity typing by reducing candidate types, incorporating multi-step revision, and providing tools for faster annotation and analysis.

## Contribution

It introduces a novel annotation framework with entity linking, multi-step revision, and management modules to enhance annotation efficiency for fine-grained entity typing.

## Key findings

- Significantly improves annotation efficiency
- Time consumption grows slowly with larger type sets
- Facilitates large-scale high-quality annotations

## Abstract

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.