Datasets:
Tasks:
Token Classification
Sub-tasks:
parsing
Languages:
Chinese
Multilinguality:
monolingual
Size Categories:
1K<n<10K
Language Creators:
found
Annotations Creators:
crowdsourced
Source Datasets:
original
Tags:
License:
| { | |
| "amttl": { | |
| "description": "Chinese word segmentation (CWS) trained from open source corpus faces dramatic performance drop\nwhen dealing with domain text, especially for a domain with lots of special terms and diverse\nwriting styles, such as the biomedical domain. However, building domain-specific CWS requires\nextremely high annotation cost. In this paper, we propose an approach by exploiting domain-invariant\nknowledge from high resource to low resource domains. Extensive experiments show that our mode\nachieves consistently higher accuracy than the single-task CWS and other transfer learning\nbaselines, especially when there is a large disparity between source and target domains.\n\nThis dataset is the accompanied medical Chinese word segmentation (CWS) dataset.\nThe tags are in BIES scheme.\n\nFor more details see https://www.aclweb.org/anthology/C18-1307/\n", | |
| "citation": "@inproceedings{xing2018adaptive,\n title={Adaptive multi-task transfer learning for Chinese word segmentation in medical text},\n author={Xing, Junjie and Zhu, Kenny and Zhang, Shaodian},\n booktitle={Proceedings of the 27th International Conference on Computational Linguistics},\n pages={3619--3630},\n year={2018}\n}\n", | |
| "homepage": "https://www.aclweb.org/anthology/C18-1307/", | |
| "license": "", | |
| "features": { | |
| "id": { | |
| "dtype": "string", | |
| "_type": "Value" | |
| }, | |
| "tokens": { | |
| "feature": { | |
| "dtype": "string", | |
| "_type": "Value" | |
| }, | |
| "_type": "Sequence" | |
| }, | |
| "tags": { | |
| "feature": { | |
| "names": [ | |
| "B", | |
| "I", | |
| "E", | |
| "S" | |
| ], | |
| "_type": "ClassLabel" | |
| }, | |
| "_type": "Sequence" | |
| } | |
| }, | |
| "builder_name": "parquet", | |
| "dataset_name": "amttl", | |
| "config_name": "amttl", | |
| "version": { | |
| "version_str": "1.0.0", | |
| "major": 1, | |
| "minor": 0, | |
| "patch": 0 | |
| }, | |
| "splits": { | |
| "train": { | |
| "name": "train", | |
| "num_bytes": 1132196, | |
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| "dataset_name": null | |
| }, | |
| "validation": { | |
| "name": "validation", | |
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| "num_examples": 822, | |
| "dataset_name": null | |
| }, | |
| "test": { | |
| "name": "test", | |
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| "num_examples": 908, | |
| "dataset_name": null | |
| } | |
| }, | |
| "download_size": 274351, | |
| "dataset_size": 1785063, | |
| "size_in_bytes": 2059414 | |
| } | |
| } |