面向斯拉夫语言的跨语言命名实体语料库
Cross-lingual Named Entity Corpus for Slavic Languages
AI总结：
本文构建涵盖六种斯拉夫语言、5017份文档和五类实体的跨语言命名实体语料库，并基于XLM-RoBERTa-large与mT5-large建立识别、分类、词元还原及链接基准。
摘要：
本文提出了一个为六种斯拉夫语言（保加利亚语、捷克语、波兰语、斯洛文尼亚语、俄语和乌克兰语）手工标注命名实体的语料库。这项工作是2017年至2023年间作为斯拉夫自然语言处理研讨会组成部分而开展的一系列共享任务的成果。该语料库包含涉及七个主题的5017份文档。这些文档标注了五类命名实体。每个实体都由类别、词元和唯一的跨语言标识符描述。我们提供了两种训练—调优数据集划分：单主题留出和跨主题。对于每种划分，我们使用基于Transformer的神经网络架构以及预训练多语言模型设定了基准：采用XLM-RoBERTa-large进行命名实体提及识别与分类，采用mT5-large进行命名实体词元还原和链接。
英文摘要：
This paper presents a corpus manually annotated with named entities for six Slavic languages - Bulgarian, Czech, Polish, Slovenian, Russian, and Ukrainian. This work is the result of a series of shared tasks, conducted in 2017-2023 as a part of the Workshops on Slavic Natural Language Processing. The corpus consists of 5 017 documents on seven topics. The documents are annotated with five classes of named entities. Each entity is described by a category, a lemma, and a unique cross-lingual identifier. We provide two train-tune dataset splits - single topic out and cross topics. For each split, we set benchmarks using a transformer-based neural network architecture with the pre-trained multilingual models - XLM-RoBERTa-large for named entity mention recognition and categorization, and mT5-large for named entity lemmatization and linking.
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。
