Zhu, Jiayi;
Ni, Pin;
Li, Yuming;
Peng, Junkun;
Dai, Zhenjin;
Le, Gangmin;
Bai, Xuming;
(2020)
An Word2vec based on Chinese Medical Knowledge.
In: Baru, Chaitanya and Huan, Jun and Khan, Latifur and Hu, Xiaohua and Ak, Ronay and Tian, Yuanyuan and Barga, Roger and Zaniolo, Carlo and Lee, Kisung and Ye, Yanfang Fanny, (eds.)
2019 IEEE International Conference on Big Data (Big Data).
(pp. pp. 6263-6265).
IEEE: Los Angeles, CA, USA.
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Abstract
Introducing a large amount of external prior domain knowledge will effectively improve the performance of the word embedded language model in downstream NLP tasks. Based on this assumption, we collect and collate a medical corpus data with about 36M (Million) characters and use the data of CCKS2019 as the test set to carry out multiple classifications and named entity recognition (NER) tasks with the generated word and character vectors. Compared with the results of BERT, our models obtained the ideal performance and efficiency results.
| Type: | Proceedings paper |
|---|---|
| Title: | An Word2vec based on Chinese Medical Knowledge |
| Event: | 2019 IEEE International Conference on Big Data (IEEE BigData 2019) |
| Location: | Los Angeles, CA |
| Dates: | 9 Dec 2019 - 12 Dec 2019 |
| ISBN-13: | 978-1-7281-0858-2 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/BigData47090.2019.9005510 |
| Publisher version: | https://doi.org/10.1109/BigData47090.2019.9005510 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Task analysis; Bit error rate; Medical diagnostic imaging; Training; Biological system modeling; Natural language processing; Computational modeling |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10159887 |
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