ORCID Profile
0000-0002-1840-3540
Current Organisation
Qilu University of Technology
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Publisher: IEEE
Date: 23-05-2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2022
Publisher: Springer International Publishing
Date: 2022
Publisher: Wiley
Date: 28-04-2020
DOI: 10.1002/CPE.5775
Abstract: The precise named entity recognition (NER) is a key component in Chinese clinical natural language processing. Although clinical NER systems have attracted widespread attention and been studied for decades, the latest NER research usually relies on a shallow text representation with one‐layer neural encoding, which fails to capture deep features and limits its performance improvement. To capture more features and encode the clinical text efficiently, we propose a deep stacked neural network for Chinese clinical NER. The neural network stacks two bidirectional long‐short term memory and gated recurrent unit layers to encode the text twice, followed by a conditional random fields (CRF) layer to recognize named entities in Chinese clinical text. Extensive empirical results on three real‐world datasets demonstrate that the proposed method significantly outperforms six state‐of‐the‐art NER methods. Especially compared with the conventional CRF model, our method has at least 3.75% F 1 ‐score improvement on these public datasets.
Publisher: Springer International Publishing
Date: 2020
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2021
Publisher: Computers, Materials and Continua (Tech Science Press)
Date: 2019
Publisher: Springer International Publishing
Date: 2022
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2021
Publisher: Computers, Materials and Continua (Tech Science Press)
Date: 2019
Publisher: Springer Science and Business Media LLC
Date: 28-04-2022
Publisher: Association for Computing Machinery (ACM)
Date: 16-07-2021
DOI: 10.1145/3450520
Abstract: The development of cognitive robotics brings an attractive scenario where humans and robots cooperate to accomplish specific tasks. To facilitate this scenario, cognitive robots are expected to have the ability to interact with humans with natural language, which depends on natural language understanding ( NLU ) technologies. As one core task in NLU, sentence semantic matching ( SSM ) has widely existed in various interaction scenarios. Recently, deep learning–based methods for SSM have become predominant due to their outstanding performance. However, each sentence consists of a sequence of words, and it is usually viewed as one-dimensional ( 1D ) text, leading to the existing available neural models being restricted into 1D sequential networks. A few researches attempt to explore the potential of 2D or 3D neural models in text representation. However, it is hard for their works to capture the complex features in texts, and thus the achieved performance improvement is quite limited. To tackle this challenge, we devise a novel 3D CNN-based SSM ( 3DSSM ) method for human–robot language interaction. Specifically, first, a specific architecture called feature cube network is designed to transform a 1D sentence into a multi-dimensional representation named as semantic feature cube. Then, a 3D CNN module is employed to learn a semantic representation for the semantic feature cube by capturing both the local features embedded in word representations and the sequential information among successive words in a sentence. Given a pair of sentences, their representations are concatenated together to feed into another 3D CNN to capture the interactive features between them to generate the final matching representation. Finally, the semantic matching degree is judged with the sigmoid function by taking the learned matching representation as the input. Extensive experiments on two real-world datasets demonstrate that 3DSSM is able to achieve comparable or even better performance over the state-of-the-art competing methods.
Publisher: IEEE
Date: 06-10-2021
Publisher: IEEE
Date: 18-07-2022
Publisher: IEEE
Date: 18-07-2022
Publisher: Elsevier BV
Date: 12-2022
Publisher: Springer Science and Business Media LLC
Date: 14-06-2021
No related grants have been discovered for Wenpeng Lu.