NERsocial: Efficient Named Entity Recognition Dataset Construction for Human-Robot Interaction Utilizing RapidNER
This addresses the problem of domain adaptation in NER for researchers and practitioners in human-robot interaction, though it appears incremental as it builds on existing methods like knowledge graphs and Elasticsearch.
The paper tackles the challenge of adapting named entity recognition (NER) to new domains by introducing RapidNER, a framework for efficient dataset construction, resulting in the NERsocial dataset with six entity types, 153K tokens, and 99.4K sentences for human-robot interaction.
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.