A Corpus for Named Entity Recognition in Chinese Novels with
  Multi-genres
Hanjie Zhao Jinge Xie Yuchen Yan Yuxiang Jia Yawen Ye Hongying Zan
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.