Medical Named Entity Recognition (NER) plays a crucial role in enhancing the efficiency of clinical work. Currently, Chinese Named Entity Recognition methods based on deep learning models have shown significant results in this area. However, due to the differences between Chinese and English, many methods that perform well on English datasets cannot be directly transferred to the Chinese context. Additionally, identifying medical entities also poses difficulties due to the comparative scarcity of specialized medical expertise. Furthermore, the prevalent models, which are typically tailored for recognizing non-nested entities, fall short of accurately identifying nested entities within Chinese medical texts. To address these issues, we propose a Chinese medical named entity recognition model based on feature fusion and multi-head biaffine transformations. By utilizing multi-head biaffine transformations to construct span matrices and applying convolutional networks on each channel to fully model adjacent span information, we solve the problem of nested entity recognition. Additionally, we significantly enhance the accuracy of medical entity recognition by introducing Chinese medical word vectors. Finally, our research involved testing on both a nested Chinese medical entity dataset (CBLUE-CMeEE) and a non-nested medical entity dataset (CCKS2019-Yidu-S4K). The experimental results show that our proposed model improves across all metrics, becoming the new state-of-the-art (SOTA) model.
In recent years, pseudo-labeling methods can reduce the difficulty of building speech recognition systems, in end-to-end automatic speech recognition (ASR). Iterative pseudo-labeling (IPL) is a classical semi-supervised algorithm that can efficiently perform multiple pseudo-labeling iterations on unlabeled data as acoustic models evolve. We incorporate the language model to generate pseudo-labeling based on IPL using the language model for decoding and data augmentation, and make new attempts on the selection of pseudo-labeling. The effectiveness of the improved approach is demonstrated by simulating low resources and standard settings and obtaining a word error rate better than IPL on the LIBRISPEECH test.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.