用快速构建法打造人机交互专用实体识别数据集
NERsocial: Efficient Named Entity Recognition Dataset Construction for Human-Robot Interaction Utilizing RapidNER
- 从通用知识图谱提取领域子图，自动构建标注数据
- 建成含153万词元、99.4万句的NERsocial数据集
- 基于Elasticsearch实现高效标注，适合快速部署新场景
将命名实体识别（NER）方法适配到新领域面临巨大挑战。我们提出RapidNER框架，通过高效数据集构建实现NER系统的快速部署。该框架包含三个关键步骤：(1) 从通用知识图谱中提取领域特定子图和三元组；(2) 汇集多源文本，构建聚焦于人机交互典型实体的NERsocial数据集；(3) 利用Elasticsearch（ES）实现注释方案以提升效率。经人工标注验证，NERsocial数据集包含六类实体、153,000个词元和99,400句，证明了RapidNER在加速数据集创建方面的有效性。
原文摘要 · Abstract (English)
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.
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