Liuke Jin

Dalian University of Technology

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Degen Huang

Dalian University of Technology

Lishuang Li

Dalian University of Technology

Dingxin Song

Lanzhou Jiaotong University

Zhenchao Jiang

Shenzhen Institutes of Advanced Technology

Rui Guo

Jiangxi Transportation Research Institute

Jing Zhang

Renmin University of China

Panpan Zhang

Soochow University

Yuxin Jiang

Shanghai First People's Hospital

shuang Li

Tianjin University of Traditional Chinese Medicine

Jing Zhang

Tianjin University

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Dalian University of Technology

Chinese Academy of Sciences

Chinese Academy of Medical Sciences & Peking Union Medical College

NARI Group (China)

University of Chinese Academy of Sciences

Shanghai Jiao Tong University

Jiangxi Transportation Research Institute

Ministry of Ecology and Environment

Chongqing Medical University

First Affiliated Hospital of Chongqing Medical University

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Biomedical named entity recognition based on extended Recurrent Neural Networks

2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2015)

Lishuang Li Liuke Jin Zhenchao Jiang Dingxin Song Degen Huang

Biomedical named entity recognition (bio-NER), which extracts important entities such as genes and proteins, has become one of the most fundamental tasks in biomedical knowledge acquisition. However, the performance of traditional NER systems is always limited to the construction of complex hand-designed features which are derived from various linguistic analyses and maybe only adapted to specified area. In this paper we mainly focus on building a simple and efficient system for bio-NER with the extended Recurrent Neural Network (RNN) which considers the predicted information from the prior node and external context information (topical information & clustering information). Extracting complex hand-designed features is skipped and replaced with word embeddings. The experiments conducted on the BioCreative II GM data set demonstrate RNN models outperform CRF model and deep neural networks (DNN); furthermore, the extended RNN model performs better than the original RNN model.

Named Entity Recognition

10.1109/bibm.2015.7359761

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Citations (53)

Biomedical named entity recognition based on recurrent neural networks with different extended methods

International Journal of Data Mining and Bioinformatics (2016)

Degen Huang Liuke Jin Dingxin Song Lishuang Li

Biomedical Named Entity Recognition (bio-NER) has become essential to the text mining and knowledge discovery tasks in biomedical field. However, the performance of traditional NER systems is limited to the construction of complex hand-designed features which are derived from various linguistic analyses and may only adapted to specified domain. In this paper, we mainly focus on building a simple and efficient system for bio-NER based on Recurrent Neural Network (RNN) where complex hand-designed features are replaced with word embeddings. Furthermore, the system is extended by the predicted information from the prior node and external context information (topical information & clustering information). During the training process, the word embeddings are fine-tuned by the neural network. The experiments conducted on the BioCreative II GM data set demonstrate RNN models outperform CRF model and Deep Neural Networks (DNNs) and the extended RNN model performs better than the original RNN, achieving 82.47% F-score.

Named Entity Recognition

Biomedical text mining

10.1504/ijdmb.2016.10000553

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Citations (0)

Biomedical named entity recognition based on recurrent neural networks with different extended methods

International Journal of Data Mining and Bioinformatics (2016)

Dingxin Song shuang Li Liuke Jin Degen Huang

Named Entity Recognition

10.1504/ijdmb.2016.079799

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Citations (10)

A distributed meta-learning system for Chinese entity relation extraction

Neurocomputing (2014)

Lishuang Li Jing Zhang Liuke Jin Rui Guo Degen Huang

Relationship extraction

Kernel (algebra)

10.1016/j.neucom.2014.07.016

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Citations (13)

Coreference resolution in biomedical texts

2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2014)

Lishuang Li Liuke Jin Zhenchao Jiang Jing Zhang Degen Huang

Coreference resolution recently plays a more and more important role for many natural language processing tasks. In this paper, we propose two methods for the biomedical coreference resolution. One is the single machine learning method (SVM ranker-learning algorithm) which selects appropriate features for the pronoun and noun phrase coreference resolution respectively. The other one is the hybrid method which adopts the rule-based method or the machine learning method for relative pronouns, non-relative pronouns and noun phrases coreference resolution respectively. Experiments are carried out on Biomedical Natural Language Process Shared Task (BioNLP-ST) ¹ 2011 coreference resolution corpus. In the first method (the single machine learning method), the F-score is 49.36%, higher than that using the same method with the features in the Reconcile system by 10.06%. In the second method (the hybrid method), the F-score is 68.61%, higher than that of the currently best system by 1.21%.

Coreference

10.1109/bibm.2014.6999392

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Citations (10)

Exploring Recurrent Neural Networks to Detect Named Entities from Biomedical Text

Lecture notes in computer science (2015)

Lishuang Li Liuke Jin Degen Huang

Named Entity Recognition

10.1007/978-3-319-25816-4_23

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Citations (12)

Recognizing Biomedical Named Entities Based on the Sentence Vector/Twin Word Embeddings Conditioned Bidirectional LSTM

Lecture notes in computer science (2016)

Lishuang Li Liuke Jin Yuxin Jiang Degen Huang

Feature Engineering

Feature (linguistics)

Named Entity Recognition

10.1007/978-3-319-47674-2_15

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Citations (27)

The Protein-Protein Interaction extraction based on full texts

2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2014)

Lishuang Li Liuke Jin Jieqiong Zheng Panpan Zhang Degen Huang

Protein-Protein Interaction (PPI) extraction from literatures is becoming a more and more significant task in the biomedical information extraction. Though many methods for PPI extraction have achieved promising results, they all concentrated on the abstracts of literatures rather than full texts. In this paper, we append full-text features, namely Location and Co-occurrence to extract PPIs from full texts. Location describes where the protein pair appears in the article. Co-occurrence is the frequency of each protein pair occurring in the article. In addition, syntactic patterns are extracted as features, and then feature selection is applied to improve the performance and reduce the dimension of feature vectors in SVM. Finally, the selected features are combined with two-level DET tree kernel. Experimental results show that the presented approach can achieve an F-score of 74.46% and an AUC of 78.50%.

Kernel (algebra)

Tree (set theory)

10.1109/bibm.2014.6999207

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Citations (4)

Training word embeddings for deep learning in biomedical text mining tasks

2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2015)

Zhenchao Jiang Lishuang Li Degen Huang Liuke Jin

Most word embedding methods are proposed with general purpose which take a word as a basic unit and learn embeddings according to words' external contexts. However, in biomedical text mining, there are many biomedical entities and syntactic chunks which contain rich domain information, and the semantic meaning of a word is also strongly related to those information. Hence, we present a biomedical domain-specific word embedding model by incorporating stem, chunk and entity to train word embeddings. We also present two deep learning architectures respectively for two biomedical text mining tasks, by which we evaluate our word embeddings and compare them with other models. Experimental results show that our biomedical domain-specific word embeddings overall outperform other general-purpose word embeddings in these deep learning methods for biomedical text mining tasks.

Word embedding

Biomedical text mining

10.1109/bibm.2015.7359756

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Citations (62)