NERsocial: Efficient Named Entity Recognition Dataset Construction for Human-Robot Interaction Utilizing RapidNER

TL;DR

This paper presents RapidNER, a framework that enables quick construction of domain-specific NER datasets for human-robot interaction by leveraging knowledge graphs, diverse texts, and efficient annotation methods.

Contribution

The paper introduces RapidNER, a novel framework that significantly accelerates NER dataset creation for specialized domains like human-robot interaction.

Findings

NERsocial dataset contains 99.4K sentences and 153K tokens.

RapidNER reduces dataset annotation time through Elasticsearch-based scheme.

Validated by human annotators, NERsocial effectively captures domain-specific entities.

Abstract

Adapting named entity recognition (NER) methods to new domains poses significant challenges. We introduce RapidNER, a framework designed for the rapid deployment of NER systems through efficient dataset construction. RapidNER operates through three key steps: (1) extracting domain-specific sub-graphs and triples from a general knowledge graph, (2) collecting and leveraging texts from various sources to build the NERsocial dataset, which focuses on entities typical in human-robot interaction, and (3) implementing an annotation scheme using Elasticsearch (ES) to enhance efficiency. NERsocial, validated by human annotators, includes six entity types, 153K tokens, and 99.4K sentences, demonstrating RapidNER's capability to expedite dataset creation.