Entities like person, location, organization are important for literary text analysis. The lack of annotated data hinders the progress of named entity recognition (NER) in literary domain. To promote the research of literary NER, we build the largest multi-genre literary NER corpus containing 263,135 entities in 105,851 sentences from 260 online Chinese novels spanning 13 different genres. Based on the corpus, we investigate characteristics of entities from different genres. We propose several baseline NER models and conduct cross-genre and cross-domain experiments. Experimental results show that genre difference significantly impact NER performance though not as much as domain difference like literary domain and news domain. Compared with NER in news domain, literary NER still needs much improvement and the Out-of-Vocabulary (OOV) problem is more challenging due to the high variety of entities in literary works.

翻译：人物、地点、组织等实体对文学文本分析至关重要。标注数据的匮乏阻碍了文学领域命名实体识别（NER）研究的进展。为促进文学NER研究，我们构建了目前最大的多体裁文学NER语料库，包含来自260部网络中文小说的105,851个句子，涵盖13种不同体裁，共计263,135个实体。基于该语料库，我们考察了不同体裁实体的特征。我们提出了若干NER基线模型，并开展了跨体裁与跨领域实验。实验结果表明，体裁差异虽不如文学领域与新闻领域等跨领域差异显著，但仍对NER性能具有重要影响。与新闻领域NER相比，文学NER仍需大幅改进，且由于文学作品中实体多样性较高，词汇外（OOV）问题的挑战更为严峻。