# A Corpus for Named Entity Recognition in Chinese Novels with Multi-genres

arXiv: https://arxiv.org/abs/2311.15509 · HTML: https://mlsanity.com/paper/2311.15509/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 15 references that are in the map.
- First posted on arXiv 27 Nov 2023.

## Builds on (most cited first, 7 of 7)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Bidirectional LSTM-CRF Models for Sequence Tagging](https://mlsanity.com/paper/1508.01991/) (2015)
- [A Survey on Deep Learning for Named Entity Recognition](https://mlsanity.com/paper/1812.09449/) (2018)
- [N-LTP: An Open-source Neural Language Technology Platform for Chinese](https://mlsanity.com/paper/2009.11616/) (2020)
- [Generalisation in Named Entity Recognition: A Quantitative Analysis](https://mlsanity.com/paper/1701.02877/) (2017)
- [A Discourse-Level Named Entity Recognition and Relation Extraction Dataset for Chinese Literature Text](https://mlsanity.com/paper/1711.07010/) (2017)
- [Extraction and Analysis of Fictional Character Networks: A Survey](https://mlsanity.com/paper/1907.02704/) (2019)

## Abstract (arXiv)

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