A Corpus for Named Entity Recognition in Chinese Novels with Multi-genres
This addresses the problem of limited annotated data for literary NER, enabling research in this domain, but it is incremental as it focuses on dataset creation and baseline models.
The authors tackled the lack of annotated data for named entity recognition (NER) in literary texts by building the largest multi-genre Chinese novel corpus with 263,135 entities across 13 genres, and found that genre differences significantly impact NER performance, though less than domain differences like literary vs. news.
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. Our data and models are open-sourced at https://github.com/hjzhao73/MultiGenre-ChineseNovel