Tasks: parsing
Task Categories: token-classification
Languages: Chinese
Multilinguality: monolingual
Size Categories: 1K<n<10K
Language Creators: found
Annotations Creators: crowdsourced
Source Datasets: original
Licenses: mit
amttl / dataset_infos.json
{"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\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": "", "license": "", "features": {"id": {"dtype": "string", "id": null, "_type": "Value"}, "tokens": {"feature": {"dtype": "string", "id": null, "_type": "Value"}, "length": -1, "id": null, "_type": "Sequence"}, "tags": {"feature": {"num_classes": 4, "names": ["B", "I", "E", "S"], "names_file": null, "id": null, "_type": "ClassLabel"}, "length": -1, "id": null, "_type": "Sequence"}}, "post_processed": null, "supervised_keys": null, "builder_name": "amttl", "config_name": "amttl", "version": {"version_str": "1.0.0", "description": null, "major": 1, "minor": 0, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 1132212, "num_examples": 3063, "dataset_name": "amttl"}, "validation": {"name": "validation", "num_bytes": 324374, "num_examples": 822, "dataset_name": "amttl"}, "test": {"name": "test", "num_bytes": 328525, "num_examples": 908, "dataset_name": "amttl"}}, "download_checksums": {"": {"num_bytes": 434357, "checksum": "9819373963ea04d1d28844d5bc83b6b0332fad8b5f2e73092bcfc58dc6d6292a"}, "": {"num_bytes": 124973, "checksum": "1a2eb461b98d2a9160baad7f76d003cc0917b998e8283bcffa52b71224dd9d17"}, "": {"num_bytes": 126204, "checksum": "aea1a8cf244cd565e94bd193a1eef7a10b16eeb0b6fbb6ed1d2fefbd55360dd6"}}, "download_size": 685534, "post_processing_size": null, "dataset_size": 1785111, "size_in_bytes": 2470645}}