InstructIE is an bilingual information extraction dataset based on topic schemas. We divide the text into 12 topics, namely, Person, Geographic_Location, Building, Works, Creature, Artificial_Object, Natural_Science, Organization, Transport, Event, Astronomy, Medicine. For each topic, we have designed corresponding schemas. We expect the model to learn a general extraction capability on InstructIE and generalize it to other domains.


The InstructIE dataset includes the following files:
- train_zh.json: Chinese training set.
- train_en.json: English training set.
- dev_zh.json: Chinese development set.
- dev_en.json: English development set.
- test_zh.json: Chinese test set.
- test_en.json: English test set.
- schema_zh.json: Schema information for 12 topics in Chinese.
- schema_en.json: Schema information for 12 topics in English. 



<b>Example of data</b>

```
{
  "id": "841ef2af4cfe766dd9295fb7daf321c299df0fd0cef14820dfcb421161eed4a1", 
  "text": "NGC1313 is a galaxy in the constellation of Reticulum. It was discovered by the Australian astronomer James Dunlop on September 27, 1826. It has a prominent uneven shape, and its axis does not completely revolve around its center. Near NGC1313, there is another galaxy, NGC1309.", 
  "relation": [
    {"head": "NGC1313", "head_type": "astronomical object type", "relation": "time of discovery", "tail": "September 27, 1826", "tail_type": "time"}, 
    {"head": "NGC1313", "head_type": "astronomical object type", "relation": "discoverer or inventor", "tail": "James Dunlop", "tail_type": "organization/human"}, 
    {"head": "NGC1313", "head_type": "astronomical object type", "relation": "of", "tail": "Reticulum", "tail_type": "astronomical object type"}
  ]
}
```


| Field       | Description                                                      |
| ----------- | ---------------------------------------------------------------- |
| id          | The unique identifier for each data point.                       |
| cate        | The category of the text's subject, with a total of 12 different thematic categories. |
| text       | The input text for the model, with the goal of extracting all the involved relationship triples. |
| relation    | Describes the relationship triples contained in the text, i.e., (head, head_type, relation, tail, tail_type). |



With the fields mentioned above, users can flexibly design and implement instructions and output formats for different information extraction needs.


## Citation

Please cite these papers if you use InstructIE in your work.

```bibtex

@article{DBLP:journals/corr/abs-2305-11527,
  author       = {Honghao Gui and
  Jintian Zhang and
  Hongbin Ye and
  Ningyu Zhang},
  title        = {InstructIE: {A} Chinese Instruction-based Information Extraction Dataset},
  journal      = {CoRR},
  volume       = {abs/2305.11527},
  year         = {2023},
  url          = {https://doi.org/10.48550/arXiv.2305.11527},
  doi          = {10.48550/arXiv.2305.11527},
  eprinttype    = {arXiv},
  eprint       = {2305.11527},
  timestamp    = {Thu, 25 May 2023 15:41:47 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2305-11527.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}


```


task_categories:text2text-generation size_categories:100M<n<1B language:en language:zh license:mit information-extraction entity relation arxiv:2305.11527 region:us
{"language": ["en", "zh"], "license": "mit", "size_categories": ["100M<n<1B"], "task_categories": ["text2text-generation"], "pretty_name": "InstructIE", "tags": ["information-extraction", "entity", "relation"]}
TAGS
#task_categories-text2text-generation #size_categories-100M<n<1B #language-English #language-Chinese #license-mit #information-extraction #entity #relation #arxiv-2305.11527 #region-us 

InstructIE is an bilingual information extraction dataset based on topic schemas. We divide the text into 12 topics, namely, Person, Geographic\_Location, Building, Works, Creature, Artificial\_Object, Natural\_Science, Organization, Transport, Event, Astronomy, Medicine. For each topic, we have designed corresponding schemas. We expect the model to learn a general extraction capability on InstructIE and generalize it to other domains.


The InstructIE dataset includes the following files:


* train\_zh.json: Chinese training set.
* train\_en.json: English training set.
* dev\_zh.json: Chinese development set.
* dev\_en.json: English development set.
* test\_zh.json: Chinese test set.
* test\_en.json: English test set.
* schema\_zh.json: Schema information for 12 topics in Chinese.
* schema\_en.json: Schema information for 12 topics in English.


**Example of data**



With the fields mentioned above, users can flexibly design and implement instructions and output formats for different information extraction needs.


Please cite these papers if you use InstructIE in your work.



TAGS
#task_categories-text2text-generation #size_categories-100M<n<1B #language-English #language-Chinese #license-mit #information-extraction #entity #relation #arxiv-2305.11527 #region-us 

passage: TAGS
#task_categories-text2text-generation #size_categories-100M<n<1B #language-English #language-Chinese #license-mit #information-extraction #entity #relation #arxiv-2305.11527 #region-us 
