├── LICENSE ├── README.md ├── TCM Code.zip ├── TCM Data Sample.zip ├── TCM_SD_train_dev.zip ├── test.json └── test_no_answer.json /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2021 Mucheng Ren 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # TCM-SD dataset and ZY-BERT 2 | 3 | The related paper "TCM-SD: A Benchmark for Probing Syndrome Differentiation via Natural Language Processing" was published in CCL2022. 4 | 5 | 6 | 7 | ![](https://s3.bmp.ovh/imgs/2022/10/13/c5b54b7696504507.png) 8 | 9 | ### Dataset Info. 10 | 11 | **TCM_SD_with_knowledge** Contains the full data! 12 | 13 | You can find more details about the dataset at ([here](https://tianchi.aliyun.com/dataset/dataDetail?dataId=139034)). 14 | 15 | **TCM Data Sample** Contains the multiple samples from TCM-SD dataset. 16 | 17 | 18 | 19 | We uploaded the corpus used for pretraining ZY-BERT, download via ([here](https://www.dropbox.com/s/jrgngr8afqz41oy/tcm_pretrain_corpus_a.rar?dl=0)). 20 | To be noticed, the current released version does not contains academic papers and journals from CNKI for some reasons(you know why:>). But I would keep updating and enriching the corpus, please keep in mind. 21 | 22 | 23 | The dataset is licensed by [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/?spm=5176.12282016.0.0.60246d92uOnf7v)。 24 | 25 | ### Model Info. 26 | 27 | Pre-trained ZY-BERT could be downloaded at [here](https://drive.google.com/file/d/1fC9geqeLk5YK9y_O-UjfIKtLeu0Iie8j/view?usp=sharing) 28 | 29 | 30 | 31 | 32 | 33 | ### Code Info. 34 | 35 | **TCM Code** contains the codes for completing syndrome differentiation task using Bert-like language model. 36 | 37 | 38 | 39 | ### Contact Info. 40 | 41 | Email: renm@nuist.edu.cn or rdoctmc@gmail.com 42 | 43 | Issue is welcome! 44 | 45 | 46 | 47 | ### Cite 48 | 49 | If you used this in your work, please cite: 50 | 51 | ``` 52 | @inproceedings{mucheng-etal-2022-tcm, 53 | title = "{TCM}-{SD}: A Benchmark for Probing Syndrome Differentiation via Natural Language Processing", 54 | author = "Ren, Mucheng and 55 | Huang, Heyan and 56 | Zhou, Yuxiang and 57 | Cao, Qianwen and 58 | Bu, Yuan and 59 | Gao, Yang", 60 | booktitle = "Proceedings of the 21st Chinese National Conference on Computational Linguistics", 61 | month = oct, 62 | year = "2022", 63 | address = "Nanchang, China", 64 | publisher = "Chinese Information Processing Society of China", 65 | url = "https://aclanthology.org/2022.ccl-1.80", 66 | pages = "908--920", 67 | abstract = "{``}Traditional Chinese Medicine (TCM) is a natural, safe, and effective therapy that has spread and been applied worldwide. The unique TCM diagnosis and treatment system requires a comprehensive analysis of a patient{'}s symptoms hidden in the clinical record written in free text. Prior studies have shown that this system can be informationized and intelligentized with the aid of artificial intelligence (AI) technology, such as natural language processing (NLP). However, existing datasets are not of sufficient quality nor quantity to support the further development of data-driven AI technology in TCM. Therefore, in this paper, we focus on the core task of the TCM diagnosis and treatment system{---}syndrome differentiation (SD){---}and we introduce the first public large-scale benchmark for SD, called TCM-SD. Our benchmark contains 54,152 real-world clinical records covering 148 syndromes. Furthermore, we collect a large-scale unlabelled textual corpus in the field of TCM and propose a domain-specific pre-trained language model, called ZYBERT. We conducted experiments using deep neural networks to establish a strong performance baseline, reveal various challenges in SD, and prove the potential of domain-specific pre-trained language model. Our study and analysis reveal opportunities for incorporating computer science and linguistics knowledge to explore the empirical validity of TCM theories.{''}", 68 | language = "English", 69 | } 70 | ``` 71 | 72 | -------------------------------------------------------------------------------- /TCM Code.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Borororo/ZY-BERT/72a89518392ec28f5dc1c8ed252a6c127ca2145b/TCM Code.zip -------------------------------------------------------------------------------- /TCM Data Sample.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Borororo/ZY-BERT/72a89518392ec28f5dc1c8ed252a6c127ca2145b/TCM Data Sample.zip -------------------------------------------------------------------------------- /TCM_SD_train_dev.zip: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Borororo/ZY-BERT/72a89518392ec28f5dc1c8ed252a6c127ca2145b/TCM_SD_train_dev.zip --------------------------------------------------------------------------------