Recognition of Disease Genetic Information from Unstructured Text Data Based on BiLSTM-CRF for Molecular Mechanisms

Gong, Li; Zhang, Xingxing; Chen, Tianyin; Zhang, Li

doi:10.1155/2021/6635027

Cited by 8 publications

(3 citation statements)

References 44 publications

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“…In the whole system, it penetrates into all aspects of the enterprise, not only requires the active participation of relevant personnel but more importantly requires technical support. Data warehouse is an important part of it, and data warehouse is an integral part of data mining technology [13,14]. e data warehouse consists of three structures that work in tandem to meet the decision-making needs of business management [15,16].…”

Section: Advanced Artificial Intelligence Model Methods For Financial...mentioning

confidence: 99%

Advanced Artificial Intelligence Model for Financial Accounting Transformation Based on Machine Learning and Enterprise Unstructured Text Data

Zheng

2022

Mobile Information Systems

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Machine learning belongs to the science of artificial intelligence, so its main exploration goal is artificial intelligence, mainly to accumulate experience and improve the relevant performance of the algorithm. ML is a complex discipline that learns and improves skills primarily through imitation. Unstructured text data refer to unstructured data in the form of text. Financial accounting is a basic work of an enterprise, mainly to provide decision-making reference information for enterprise managers to ensure the normal operation of the enterprise. Financial accounting is a management activity that monitors the business and provides relevant information to the relevant authorities. This paper aims to study the advanced artificial intelligence model of financial accounting transformation based on machine learning and enterprise unstructured text data, expecting to use machine learning and unstructured text technology to upgrade the management of financial accounting and improve its analysis level. In this paper, an improved KMP algorithm is proposed for the keyword matching problem of unstructured data. Then, the data crawling technology of unstructured text is studied. It mainly uses the crawler technology based on the Python language and obtains a large amount of information on the Internet by formulating appropriate regular expressions. The algorithm is used to mine the data set and generate frequent itemsets. The association rules mining algorithm is proposed and implemented, and the association rules with practical reference value are obtained. The test leads in this document indicate that the return on assets of the enterprise is −11.2%, the net interest rate on equity is −44.5%, and the business profit rate is −12.1%. This shows that the profitability of the company has been declining in recent years, and there is even the risk of bankruptcy.

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Section: Advanced Artificial Intelligence Model Methods For Financial...mentioning

confidence: 99%

Advanced Artificial Intelligence Model for Financial Accounting Transformation Based on Machine Learning and Enterprise Unstructured Text Data

Zheng

2022

Mobile Information Systems

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“…The implied layers in the past and the implied layers in the future are independent of each other, so BILSTM can better explore the temporal characteristics of the data. BILSTM structure diagram is shown in Figure 2 (Gong et al, 2021 ; Hou and Zhu, 2021 ).…”

Section: Ngo-bilstm Modelmentioning

confidence: 99%

Face expression recognition based on NGO-BILSTM model

Zhong

Chen

2023

Front. Neurorobot.

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IntroductionFacial expression recognition has always been a hot topic in computer vision and artificial intelligence. In recent years, deep learning models have achieved good results in accurately recognizing facial expressions. BILSTM network is such a model. However, the BILSTM network's performance depends largely on its hyperparameters, which is a challenge for optimization.MethodsIn this paper, a Northern Goshawk optimization (NGO) algorithm is proposed to optimize the hyperparameters of BILSTM network for facial expression recognition. The proposed methods were evaluated and compared with other methods on the FER2013, FERplus and RAF-DB datasets, taking into account factors such as cultural background, race and gender.ResultsThe results show that the recognition accuracy of the model on FER2013 and FERPlus data sets is much higher than that of the traditional VGG16 network. The recognition accuracy is 89.72% on the RAF-DB dataset, which is 5.45, 9.63, 7.36, and 3.18% higher than that of the proposed facial expression recognition algorithms DLP-CNN, gACNN, pACNN, and LDL-ALSG in recent 2 years, respectively.DiscussionIn conclusion, NGO algorithm effectively optimized the hyperparameters of BILSTM network, improved the performance of facial expression recognition, and provided a new method for the hyperparameter optimization of BILSTM network for facial expression recognition.

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“…BiLSTM already has a variety of applications in technology. BiLSTM-based systems can learn to translate languages [ 22 ], document summaries [ 23 ], speech recognition [ 24 ], dialogue system [ 25 ], predicting disease [ 26 ], and so on. In sentiment classification, the BiLSTM, BiGRU, and CNN model are integrated and proposed for sentiment classification [ 5 ].…”

Section: Related Workmentioning

confidence: 99%

Integrating BERT Embeddings and BiLSTM for Emotion Analysis of Dialogue

Gou

Yan²

2023

Computational Intelligence and Neuroscience

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Dialogue system is an important application of natural language processing in human-computer interaction. Emotion analysis of dialogue aims to classify the emotion of each utterance in dialogue, which is crucially important to dialogue system. In dialogue system, emotion analysis is helpful to the semantic understanding and response generation and is great significance to the practical application of customer service quality inspection, intelligent customer service system, chatbots, and so on. However, it is challenging to solve the problems of short text, synonyms, neologisms, and reversed word order for emotion analysis in dialogue. In this paper, we analyze that the feature modeling of different dimensions of dialogue utterances is helpful to achieve more accurate sentiment analysis. Based on this, we propose the BERT (bidirectional encoder representation from transformers) model that is used to generate word-level and sentence-level vectors, and then, word-level vectors are combined with BiLSTM (bidirectional long short-term memory) that can better capture bidirectional semantic dependencies, and word-level and sentence-level vectors are connected and inputted to linear layer to determine emotions in dialogue. The experimental results on two real dialogue datasets show that the proposed method significantly outperforms the baselines.

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Recognition of Disease Genetic Information from Unstructured Text Data Based on BiLSTM-CRF for Molecular Mechanisms

Cited by 8 publications

References 44 publications

Advanced Artificial Intelligence Model for Financial Accounting Transformation Based on Machine Learning and Enterprise Unstructured Text Data

Advanced Artificial Intelligence Model for Financial Accounting Transformation Based on Machine Learning and Enterprise Unstructured Text Data

Face expression recognition based on NGO-BILSTM model

Integrating BERT Embeddings and BiLSTM for Emotion Analysis of Dialogue

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