Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and Languages for Open Named Entity Recognition

• URL: http://arxiv.org/abs/2406.11192v1
• Date: Mon, 17 Jun 2024 03:57:35 GMT
• Title: Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and Languages for Open Named Entity Recognition
• Authors: Yuming Yang, Wantong Zhao, Caishuang Huang, Junjie Ye, Xiao Wang, Huiyuan Zheng, Yang Nan, Yuran Wang, Xueying Xu, Kaixin Huang, Yunke Zhang, Tao Gui, Qi Zhang, Xuanjing Huang,
• Abstract summary: We present B2NERD, a cohesive and efficient dataset for Open NER. We detect inconsistent entity definitions across datasets and clarify them by distinguishable label names to construct a universal taxonomy of 400+ entity types. Our B2NER models, trained on B2NERD, outperform GPT-4 by 6.8-12.0 F1 points and surpass previous methods in 3 out-of-domain benchmarks across 15 datasets and 6 languages.
• Score: 40.23783832224238
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Open Named Entity Recognition (NER), which involves identifying arbitrary types of entities from arbitrary domains, remains challenging for Large Language Models (LLMs). Recent studies suggest that fine-tuning LLMs on extensive NER data can boost their performance. However, training directly on existing datasets faces issues due to inconsistent entity definitions and redundant data, limiting LLMs to dataset-specific learning and hindering out-of-domain generalization. To address this, we present B2NERD, a cohesive and efficient dataset for Open NER, normalized from 54 existing English or Chinese datasets using a two-step approach. First, we detect inconsistent entity definitions across datasets and clarify them by distinguishable label names to construct a universal taxonomy of 400+ entity types. Second, we address redundancy using a data pruning strategy that selects fewer samples with greater category and semantic diversity. Comprehensive evaluation shows that B2NERD significantly improves LLMs' generalization on Open NER. Our B2NER models, trained on B2NERD, outperform GPT-4 by 6.8-12.0 F1 points and surpass previous methods in 3 out-of-domain benchmarks across 15 datasets and 6 languages.

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