MultiCoNER v2：一个用于细粒度且含噪命名实体识别的大型多语言数据集
MultiCoNER v2: a Large Multilingual dataset for Fine-grained and Noisy Named Entity Recognition
AI总结：
提出MULTICONER V2多语言细粒度命名实体识别数据集，涵盖12种语言33个类别，通过XLM-RoBERTa基线评估发现细粒度分类和实体噪声对性能的显著影响。
摘要：
我们提出了MULTICONER V2，这是一个用于细粒度命名实体识别的数据集，涵盖12种语言的33个实体类别，支持单语和双语设置。该数据集旨在解决NER中的实际挑战：(i)有效处理包含电影片名等复杂实体的细粒度类别，(ii)处理因打字错误或OCR错误产生的噪声导致的性能下降。数据集基于Wikipedia和Wikidata等开放资源编译，已公开发布。基于XLM-RoBERTa基线的评估突显了MULTICONER V2带来的独特挑战：(i)细粒度分类体系难度较高，各语言平均得分较低，宏F1=0.63；(ii)破坏策略显著损害性能，实体破坏导致性能比非实体破坏低9%。这表明实体噪声比上下文噪声影响更大。
英文摘要：
We present MULTICONER V2, a dataset for fine-grained Named Entity Recognition covering 33 entity classes across 12 languages, in both monolingual and multilingual settings. This dataset aims to tackle the following practical challenges in NER: (i) effective handling of fine-grained classes that include complex entities like movie titles, and (ii) performance degradation due to noise generated from typing mistakes or OCR errors. The dataset is compiled from open resources like Wikipedia and Wikidata, and is publicly available. Evaluation based on the XLM-RoBERTa baseline highlights the unique challenges posed by MULTICONER V2: (i) the fine-grained taxonomy is challenging, where the scores are low with macro-F1=0.63 (across all languages), and (ii) the corruption strategy significantly impairs performance, with entity corruption resulting in 9% lower performance relative to non-entity corruptions across all languages. This highlights the greater impact of entity noise in contrast to context noise.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
