Character level and word level embedding with bidirectional LSTM – Dynamic recurrent neural network for biomedical named entity recognition from literature

Gajendran, Sudhakaran; Manjula, D.; Sugumaran, Vijayan

doi:10.1016/j.jbi.2020.103609

Cited by 43 publications

(24 citation statements)

References 30 publications

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“…The extracted feature vectors are fed into the Bi-LSTM in both forward and backward LSTMs to learn the OD tolerance of decomposed shaft data in sequence. The Bi-LSTM cells cross verify the vectors against each other to ensure that the correct vector values are fed into the networks [31]. The CRF layer jointly decodes the best chain of labeled OD tolerance zones for a given shaft part.…”

Section: Cnn-bilstm-crf For Od Tolerancementioning

confidence: 99%

Learning tolerance specification of shaft part based on CNN-BiLSTM-CRF

Xiong

2022

Preprint

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Determining reasonable tolerance patterns requires much knowledge and experience to balance the conflicting requirements. In previous studies, the process of tolerance specification has been based on the engineer’s experience and empirical data handed down from earlier projects. This paper proposes a learning model of tolerance specification of shaft part using deep learning architecture named CNN-BiLSTM-CRF, which is a combination of a Convolutional Neural Network, BiDirection Long Short-Term Memory network, and Conditional Random Field model. The CNN layer extracts the features from the CAD model; the Bi-LSTM layer composes the nonlinear relationship between tolerance specification and the features embedded by the CNN layer; the CRF layer predicts the tolerance sequence of the whole shaft part by jointly considering relations between neighbor tolerances. The proposed method directly takes the CAD model as the input and learns the tolerance specification from previous successful cases. Therefore, it does not require engineers to have any empirical knowledge.

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Section: Cnn-bilstm-crf For Od Tolerancementioning

confidence: 99%

Learning tolerance specification of shaft part based on CNN-BiLSTM-CRF

Xiong

2022

Preprint

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“…In the early time, many researches mainly focused on the traditional rule methods [4][5]. In recent years, with the development and maturity of artificial intelligence technology, many artificial intelligence technologies have been applied to the named entities and relations recognition, which include maximum entropy Markov model(MEMM) [6][7], conditional random field(CRF) [8][9][10], convolutional neural network(CNN) [11][12][13] ， recurrent neural network(RNN) [14][15][16] and its improvement long short term memory(LSTM) model [17][18][19], as well as LSTM-CRF [20][21] model based on the combination of LSTM and conditional random field, etc.…”

Section: Introductionmentioning

confidence: 99%

Learning Ostensive Knowledge of Shaft Parts based on CRF model

Xiong

2022

Preprint

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During the process of mechanical design, there exit a large amount of knowledge and information that needs to be obtained by consulting and referencing past cases. This type of knowledge is called ostensive knowledge in philosophy, and belongs to a typical relational tacit knowledge. On the one hand, ostensive knowledge cannot be expressed through words, numbers, scientific formulas, and coding procedures. On the other hand, knowledge is parasitic in cases and cannot exist independently from them. This paper introduces ostensive knowledge to the mechanical design field for the first time, and points out that the learning model of ostensive knowledge must have the memories of cases and the ability of case-based reasoning. On this basis, this paper focuses on the shaft parts, and proposes an ostensive knowledge learning model for shaft parts based on the conditional random field model. Finally, an experiment is carried out using actual engineering cases to analyze and verify the model, which shows that the model has good memory and reasoning capabilities for shaft parts cases.

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“…We organized the differences into 3 main points. First, the RNA sequences are a combination of multiple meaningful and meaningless sequences, where the meaningful units are embedded into the entire background sequences, not like the words that form a certain grammatical structure in order [16] . An RNA typically has a large variety of functions enabled by meaningful units, such as the ability to form high-level structures and to recruit other components [17] .…”

Section: Introductionmentioning

confidence: 99%

Attention-based deep multiple instances learning for classifying circular RNA and other long non-coding RNA

Liu

Peng

et al. 2021

Preprint

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Circular RNA (circRNA) is a distinguishable circular formed long non-coding RNA (lncRNA), which has specific roles in transcriptional regulation, multiple biological processes. The identification of circRNA from other lncRNA is necessary for relevant research. In this study, we designed attention-based multi-instance learning (MIL) network architecture, which can be fed with raw sequence, to learn the sparse features in sequences and accomplish the identification task for circRNAs. The model outperformed previously reported models. Following the effectiveness validation of the attention score by the handwritten digit dataset, the key sequence loci underlying circRNAs recognition were obtained based on the corresponding attention score. Moreover, the motif enrichment analysis of the extracted key sequences identified some of the key motifs for circRNA formation. In conclusion, we designed a deep learning network architecture suitable for gene sequence learning with sparse features and implemented to the circRNA identification, and the network has a strong representation capability with its indication of some key loci.

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Character level and word level embedding with bidirectional LSTM – Dynamic recurrent neural network for biomedical named entity recognition from literature

Cited by 43 publications

References 30 publications

Learning tolerance specification of shaft part based on CNN-BiLSTM-CRF

Learning tolerance specification of shaft part based on CNN-BiLSTM-CRF

Learning Ostensive Knowledge of Shaft Parts based on CRF model

Attention-based deep multiple instances learning for classifying circular RNA and other long non-coding RNA

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