Simple Questions Generate Named Entity Recognition Datasets

• URL: http://arxiv.org/abs/2112.08808v1
• Date: Thu, 16 Dec 2021 11:44:38 GMT
• Title: Simple Questions Generate Named Entity Recognition Datasets
• Authors: Hyunjae Kim, Jaehyo Yoo, Seunghyun Yoon, Jinhyuk Lee, Jaewoo Kang
• Abstract summary: This work introduces an ask-to-generate approach, which automatically generates NER datasets by asking simple natural language questions. Our models largely outperform previous weakly supervised models on six NER benchmarks across four different domains. Formulating the needs of NER with natural language also allows us to build NER models for fine-grained entity types such as Award.
• Score: 18.743889213075274
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Named entity recognition (NER) is a task of extracting named entities of specific types from text. Current NER models often rely on human-annotated datasets requiring the vast engagement of professional knowledge on the target domain and entities. This work introduces an ask-to-generate approach, which automatically generates NER datasets by asking simple natural language questions that reflect the needs for entity types (e.g., Which disease?) to an open-domain question answering system. Without using any in-domain resources (i.e., training sentences, labels, or in-domain dictionaries), our models solely trained on our generated datasets largely outperform previous weakly supervised models on six NER benchmarks across four different domains. Surprisingly, on NCBI-disease, our model achieves 75.5 F1 score and even outperforms the previous best weakly supervised model by 4.1 F1 score, which utilizes a rich in-domain dictionary provided by domain experts. Formulating the needs of NER with natural language also allows us to build NER models for fine-grained entity types such as Award, where our model even outperforms fully supervised models. On three few-shot NER benchmarks, our model achieves new state-of-the-art performance.

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