https://github.com/lancopku/Chinese-Literature-NER-RE-Dataset

A Discourse-Level Named Entity Recognition and Relation Extraction Dataset for Chinese Literature Text
https://github.com/lancopku/Chinese-Literature-NER-RE-Dataset

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A Discourse-Level Named Entity Recognition and Relation Extraction Dataset for Chinese Literature Text

• Host: GitHub
• URL: https://github.com/lancopku/Chinese-Literature-NER-RE-Dataset
• Owner: lancopku
• Created: 2017-11-19T07:44:31.000Z (over 6 years ago)
• Default Branch: master
• Last Pushed: 2018-05-14T02:24:09.000Z (about 6 years ago)
• Last Synced: 2024-01-20T14:10:48.022Z (5 months ago)
• Size: 7.13 MB
• Stars: 397
• Watchers: 21
• Forks: 83
• Open Issues: 7
• Metadata Files:
  • Readme: README.md

Lists

• awesome-chinese-nlp - Chinese-Literature-NER-RE-Dataset - Level Named Entity Recognition and Relation Extraction Dataset for Chinese Literature Text (Corpus 中文语料 / Multi-Modal Representation & Retrieval 多模态表征与检索)
• awesome-nlp-resource - Chinese-Literature-NER-RE - level Named Entity Recognition and Relation Extraction dataset for Chinese literature text. It contains 726 articles, 29,096 sentences and over 100,000 characters in total. (Uncategorized / Uncategorized)
• awesome-nlp-chinese-corpus - 中国文学文本数据集
• StarryDivineSky - lancopku/Chinese-Literature-NER-RE-Dataset

README

        
# Chinese-Literature-NER-RE-Dataset

A Discourse-Level Named Entity Recognition and Relation Extraction Dataset  for Chinese Literature Text

We provide a new Chinese literature dataset for Named Entity Recognition (NER) and Relation Extraction (RE). The dataset is described at https://arxiv.org/pdf/1711.07010.pdf

# Tagging Set

We define 7 entity tags and 9 relation tags based on several available NER and RE datasets but with some additional categories specific to Chinese literature text. 

![ner.png](https://github.com/lancopku/Chinese-Literature-NER-RE-Dataset/blob/master/ner.png)

![relation.png](https://github.com/lancopku/Chinese-Literature-NER-RE-Dataset/blob/master/relation.png)

# Annotation Format

## Entity

Each entity is identified by T tag, which takes several attributes. 

Id: a unique number identifying the entity within the document. It starts at 0, and is incremented every time a new entity is identified within the same document. 

Type: one of the entity tags.

 

Begin Index: the begin index of an entity. It starts at 0, and is incremented every character.

End Index: the end index of an entity. It starts at 0, and is incremented every character.

Value: words being referred to an identifiable object.

## Relation

Each relation is identified by R tag, which can take several attributes: 

Id: a unique number identifying the relation within the document. It starts at 0, and is incremented every time a new relation is identified within the same document. 

Arg1 and Arg2: two entities associated with a relation.

Type: one of the relation tags.

## Citation

@inproceedings{dnerre,

author = {Jingjing Xu and Ji Wen and Xu Sun and Qi Su},

title = {A Discourse-Level Named Entity Recognition and Relation Extraction Dataset for Chinese Literature Text}, 

journal = {CoRR},

volume = {abs/1711.07010},

year = {2017},

url = http://arxiv.org/abs/1711.07010

}