task_categories:token-classification task_ids:parsing annotations_creators:crowdsourced language_creators:found multilinguality:monolingual size_categories:1K<n<10K source_datasets:original language:zh license:mit region:us
Chinese word segmentation (CWS) trained from open source corpus faces dramatic performance drop
when dealing with domain text, especially for a domain with lots of special terms and diverse
writing styles, such as the biomedical domain. However, building domain-specific CWS requires
extremely high annotation cost. In this paper, we propose an approach by exploiting domain-invariant
knowledge from high resource to low resource domains. Extensive experiments show that our mode
achieves consistently higher accuracy than the single-task CWS and other transfer learning
baselines, especially when there is a large disparity between source and target domains.

This dataset is the accompanied medical Chinese word segmentation (CWS) dataset.
The tags are in BIES scheme.

For more details see https://www.aclweb.org/anthology/C18-1307/
@inproceedings{xing2018adaptive,
  title={Adaptive multi-task transfer learning for Chinese word segmentation in medical text},
  author={Xing, Junjie and Zhu, Kenny and Zhang, Shaodian},
  booktitle={Proceedings of the 27th International Conference on Computational Linguistics},
  pages={3619--3630},
  year={2018}
}
---
annotations_creators:
- crowdsourced
language_creators:
- found
language:
- zh
license:
- mit
multilinguality:
- monolingual
size_categories:
- 1K<n<10K
source_datasets:
- original
task_categories:
- token-classification
task_ids:
- parsing
pretty_name: AMTTL
dataset_info:
  features:
  - name: id
    dtype: string
  - name: tokens
    sequence: string
  - name: tags
    sequence:
      class_label:
        names:
          '0': B
          '1': I
          '2': E
          '3': S
  config_name: amttl
  splits:
  - name: train
    num_bytes: 1132212
    num_examples: 3063
  - name: validation
    num_bytes: 324374
    num_examples: 822
  - name: test
    num_bytes: 328525
    num_examples: 908
  download_size: 685534
  dataset_size: 1785111
---

# Dataset Card for AMTTL

## Table of Contents
- [Dataset Description](#dataset-description)
  - [Dataset Summary](#dataset-summary)
  - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
  - [Languages](#languages)
- [Dataset Structure](#dataset-structure)
  - [Data Instances](#data-instances)
  - [Data Fields](#data-fields)
  - [Data Splits](#data-splits)
- [Dataset Creation](#dataset-creation)
  - [Curation Rationale](#curation-rationale)
  - [Source Data](#source-data)
  - [Annotations](#annotations)
  - [Personal and Sensitive Information](#personal-and-sensitive-information)
- [Considerations for Using the Data](#considerations-for-using-the-data)
  - [Social Impact of Dataset](#social-impact-of-dataset)
  - [Discussion of Biases](#discussion-of-biases)
  - [Other Known Limitations](#other-known-limitations)
- [Additional Information](#additional-information)
  - [Dataset Curators](#dataset-curators)
  - [Licensing Information](#licensing-information)
  - [Citation Information](#citation-information)
  - [Contributions](#contributions)

## Dataset Description

- **Homepage:** [Github](https://github.com/adapt-sjtu/AMTTL/tree/master/medical_data)
- **Repository:** [Github](https://github.com/adapt-sjtu/AMTTL/tree/master/medical_data)
- **Paper:** [Aclweb](http://aclweb.org/anthology/C18-1307)
- **Leaderboard:**
- **Point of Contact:**

### Dataset Summary

[More Information Needed]

### Supported Tasks and Leaderboards

[More Information Needed]

### Languages

[More Information Needed]

## Dataset Structure

### Data Instances

[More Information Needed]

### Data Fields

[More Information Needed]

### Data Splits

[More Information Needed]

## Dataset Creation

### Curation Rationale

[More Information Needed]

### Source Data

#### Initial Data Collection and Normalization

[More Information Needed]

#### Who are the source language producers?

[More Information Needed]

### Annotations

#### Annotation process

[More Information Needed]

#### Who are the annotators?

[More Information Needed]

### Personal and Sensitive Information

[More Information Needed]

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed]

### Discussion of Biases

[More Information Needed]

### Other Known Limitations

[More Information Needed]

## Additional Information

### Dataset Curators

[More Information Needed]

### Licensing Information

[More Information Needed]

### Citation Information
```bibtex
@inproceedings{xing2018adaptive,
  title={Adaptive multi-task transfer learning for Chinese word segmentation in medical text},
  author={Xing, Junjie and Zhu, Kenny and Zhang, Shaodian},
  booktitle={Proceedings of the 27th International Conference on Computational Linguistics},
  pages={3619--3630},
  year={2018}
}
```
### Contributions

Thanks to [@JetRunner](https://github.com/JetRunner) for adding this dataset.