Recognizing irregular entities in biomedical text via deep neural networks
By a News Reporter-Staff News Editor at Journal of Robotics & Machine Learning -- Current study results on Pattern Analysis have been published. According to news reporting out of Hubei, People’s Republic of China, by VerticalNews editors, research stated, “Named entity recognition (NER) is an important task for biomedical text mining. Most prior work focused on recognizing regular entities that consist of continuous word sequences and are not overlapped with each other.”
Funders for this research include National Natural Science Foundation of China, China Postdoctoral Science Foundation.
Our news journalists obtained a quote from the research from Wuhan University, “In this paper, we propose a neural network model called Bi-LSTM-CRF that consists of bidirectional (Bi) long short-term memories (LSTMs) and conditional random fields (CRFs) to identify regular entities and the components of irregular entities. Then the components are combined to build final irregular entities according to manually designed rules. Furthermore, we propose a novel model called NerOne that consists of the Bi-LSTM-CRF network and another Bi-LSTM network. The Bi-LSTM-CRF network performs the same task as the aforementioned model, and the Bi-LSTM network determines whether two components should be combined. Therefore, NerOne automatically combines the components instead of using manually designed rules. We evaluate our models on two datasets for recognizing regular and irregular biomedical entities. Experimental results show that, with less feature engineering, the performances of our models are comparable with those of state-of-the-art systems. We show that the method of automatically combining the components is as effective as the method of manually designing rules.”
According to the news editors, the research concluded: “Our work can facilitate the research on biomedical text mining.”
For more information on this research see: Recognizing irregular entities in biomedical text via deep neural networks. Pattern Recognition Letters , 2018;105():105-113. Pattern Recognition Letters can be contacted at: Elsevier Science Bv, PO Box 211, 1000 Ae Amsterdam, Netherlands. (Elsevier - www.elsevier.com; Pattern Recognition Letters - http://www.journals.elsevier.com/pattern-recognition-letters/)
Our news journalists report that additional information may be obtained by contacting F. Li, Wuhan University, Sch Comp, Wuhan, Hubei, People’s Republic of China. Additional authors for this research include M.S. Zhang, B. Tian, B. Chen, G.H. Fu and D.H. Ji.
The direct object identifier (DOI) for that additional information is: https://doi.org/10.1016/j.patrec.2017.06.009. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.
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CITATION: (2018-04-23), Investigators from Wuhan University Target Pattern Analysis (Recognizing irregular entities in biomedical text via deep neural networks), Journal of Robotics & Machine Learning, 152, ISSN: 1944-186X, BUTTER® ID: 015547991
From the newsletter Journal of Robotics & Machine Learning.
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