Recognition of bacteria named entity using conditional random fields in Spark

Wang, Xiaoyan; Li, Yichuan; He, Tingting; Jiang, Xingpeng; Hu, Xiaohua

doi:10.1186/s12918-018-0625-3

Cited by 10 publications

(5 citation statements)

References 11 publications

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“…The experimental results are shown in Table 4. Model 1 and Model 2 were proposed by Wang [11, 12], and their models were based on traditional machine learning methods. Therefore, they manually extracted 43 groups of features, and then achieved good results on the dataset through feature combination and selection.…”

Section: Resultsmentioning

confidence: 99%

“…Wang [11, 12] proposed a method of bacterial named entity recognition based on conditional random fields (CRF) and dictionary, which contains more than 40 features (word features, prefixes, suffixes, POS, etc.). The model effect was optimized after selecting the best combinations of 35 features, in the meanwhile, the computing efficiency of this model was greatly improved by deploying the model on Spark platform.…”

Section: Introductionmentioning

confidence: 99%

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A hybrid deep learning framework for bacterial named entity recognition with domain features

Liu

Zhong

et al. 2019

BMC Bioinformatics

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BackgroundMicrobes have been shown to play a crucial role in various ecosystems. Many human diseases have been proved to be associated with bacteria, so it is essential to extract the interaction between bacteria for medical research and application. At the same time, many bacterial interactions with certain experimental evidences have been reported in biomedical literature. Integrating this knowledge into a database or knowledge graph could accelerate the progress of biomedical research. A crucial and necessary step in interaction extraction (IE) is named entity recognition (NER). However, due to the specificity of bacterial naming, there are still challenges in bacterial named entity recognition.ResultsIn this paper, we propose a novel method for bacterial named entity recognition, which integrates domain features into a deep learning framework combining bidirectional long short-term memory network and convolutional neural network. When domain features are not added, F1-measure of the model achieves 89.14%. After part-of-speech (POS) features and dictionary features are added, F1-measure of the model achieves 89.7%. Hence, our model achieves an advanced performance in bacterial NER with the domain features.ConclusionsWe propose an efficient method for bacterial named entity recognition which combines domain features and deep learning models. Compared with the previous methods, the effect of our model has been improved. At the same time, the process of complex manual extraction and feature design are significantly reduced.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A hybrid deep learning framework for bacterial named entity recognition with domain features

Liu

Zhong

et al. 2019

BMC Bioinformatics

Self Cite

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“…Even if expert reviews, a massive medical record will be very long and tiring. Artificial intelligence has become a solution for processing patient data more quickly Biomedical and chemical [14,41,[204][205][206][207] Network and security [57,[208][209][210][211][212][213] Biology [59,[214][215][216][217][218][219][220] Chemistry [58,74,[221][222][223][224] Geoscience [47,48,225] Business and economics [40,81,226] History and culture [316-320] Agriculture [99,[321][322][323] Law [83,227] Social media [108,109] Automotive and engineering [98,[325][326][327][328] Military [104] Neuroscience [228] Sport science…”

Section: Ner Research Application Domainmentioning

confidence: 99%

Systematic Literature Review on Named Entity Recognition: Approach, Method, and Application

Warto,

Rustad,

Shidik

et al. 2024

Stat., optim. inf. comput.

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Named entity recognition (NER) is one of the preprocessing stages in natural language processing (NLP), which functions to detect and classify entities in the corpus. NER results are used in various NLP applications, including sentiment analysis, text summarization, chatbot, machine translation, and question answering. Several previous reviews partially discussed NER, for instance, NER reviews in specific domains, NER classification, and NER deep learning. This paper provides a comprehensive and systematic review on NER topic studies published from 2011 to 2020. The main contribution of this review is to present a comprehensive systematic literature review on NER from preprocessing techniques, datasets, application domains, feature extraction techniques, approaches, methods, and evaluation techniques. The result concludes that the deep learning approach and the Bi-directional long short-term memory with a conditional random field (Bi-LSTM-CRF) method are the most interesting methods among NER researchers. At the same time, medical and health are NER researchers' most popular domains. These developments have also led to an increasing number of public datasets in the medical and health fields. At the end of this review, we recommend some opportunities and challenges for NER research going forward.

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“…The semantic components such as topics, terms and document classes are represented as potential functions of an MRF [108]. Biomedical literature mining strategies using MRFs were also developed to study automated recognition of bacteria named entities [109] to curate experimental databases on microbial interactions. Related methods were previously used to identify gene and protein mentions in the literature using CRFs [110].…”

Section: Application Box Iii: Inference Of Tissue-specific Transcriptional Regulatory Networkmentioning

confidence: 99%

Random Fields in Physics, Biology and Data Science

Hernández‐Lemus

2021

Front. Phys.

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A random field is the representation of the joint probability distribution for a set of random variables. Markov fields, in particular, have a long standing tradition as the theoretical foundation of many applications in statistical physics and probability. For strictly positive probability densities, a Markov random field is also a Gibbs field, i.e., a random field supplemented with a measure that implies the existence of a regular conditional distribution. Markov random fields have been used in statistical physics, dating back as far as the Ehrenfests. However, their measure theoretical foundations were developed much later by Dobruschin, Lanford and Ruelle, as well as by Hammersley and Clifford. Aside from its enormous theoretical relevance, due to its generality and simplicity, Markov random fields have been used in a broad range of applications in equilibrium and non-equilibrium statistical physics, in non-linear dynamics and ergodic theory. Also in computational molecular biology, ecology, structural biology, computer vision, control theory, complex networks and data science, to name but a few. Often these applications have been inspired by the original statistical physics approaches. Here, we will briefly present a modern introduction to the theory of random fields, later we will explore and discuss some of the recent applications of random fields in physics, biology and data science. Our aim is to highlight the relevance of this powerful theoretical aspect of statistical physics and its relation to the broad success of its many interdisciplinary applications.

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Recognition of bacteria named entity using conditional random fields in Spark

Cited by 10 publications

References 11 publications

A hybrid deep learning framework for bacterial named entity recognition with domain features

A hybrid deep learning framework for bacterial named entity recognition with domain features

Systematic Literature Review on Named Entity Recognition: Approach, Method, and Application

Random Fields in Physics, Biology and Data Science

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