Annotating the Literature with Disease Ontology

Yang, Li; Zhou, Yanhong; Zheng, Ying

doi:10.1049/cje.2017.09.020

Cited by 7 publications

(3 citation statements)

References 6 publications

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“…Data were divided into two types: a) Entity, including ID, name, symbol, definition, xref, synonyms, parentId, isParent, and other attributes. Next, we updated the isParent attribute to an entity that had an inheritance (is-a) hierarchy, such as Disease Ontology [30] , to determine whether the current node contained child nodes; b) For the entity relations, we used the form of triples to store data, that is, entity-relationship-entity, part of the entity-relationship data, including inferScore attribute, was used to measure the credibility of this relationship.…”

Section: Data Fusionmentioning

confidence: 99%

BERST: An Engine and Tool for Exploring Biomedical Entities and Relationships

Bai

Yan

Chen³

et al. 2019

Chinese Journal of Electronics

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Section: Data Fusionmentioning

confidence: 99%

BERST: An Engine and Tool for Exploring Biomedical Entities and Relationships

Bai

Yan

Chen³

et al. 2019

Chinese Journal of Electronics

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“…This method usually requires human intervention to obtain the sentiment category of the input text. Traditional machine learning methods commonly used include naive bayes, support vector machine, maximum entropy, random forest and conditional random fields model [18], [19].…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis for E-Commerce Product Reviews in Chinese Based on Sentiment Lexicon and Deep Learning

et al. 2020

Self Cite

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In recent years, with the rapid development of Internet technology, online shopping has become a mainstream way for users to purchase and consume. Sentiment analysis of a large number of user reviews on e-commerce platforms can effectively improve user satisfaction. This paper proposes a new sentiment analysis model-SLCABG, which is based on the sentiment lexicon and combines Convolutional Neural Network (CNN) and attention-based Bidirectional Gated Recurrent Unit (BiGRU). In terms of methods, the SLCABG model combines the advantages of sentiment lexicon and deep learning technology, and overcomes the shortcomings of existing sentiment analysis model of product reviews. The SLCABG model combines the advantages of the sentiment lexicon and deep learning techniques. First, the sentiment lexicon is used to enhance the sentiment features in the reviews. Then the CNN and the Gated Recurrent Unit (GRU) network are used to extract the main sentiment features and context features in the reviews and use the attention mechanism to weight. And finally classify the weighted sentiment features. In terms of data, this paper crawls and cleans the real book evaluation of dangdang.com, a famous Chinese e-commerce website, for training and testing, all of which are based on Chinese. The scale of the data has reached 100000 orders of magnitude, which can be widely used in the field of Chinese sentiment analysis. The experimental results show that the model can effectively improve the performance of text sentiment analysis.

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“…In the text error detection subtask, we divide the text characters to be detected into 0 and 1 categories, 0 means that the character is correct, 1 means the character is wrong, and then combined with LSTM and CRF [30] technology to train the Bi-LSTM model to handle the text error detection task. In the text error correction subtask, we first construct the replacement character set of the wrong character, then construct the Bi-LSTM model, input the replacement character of the wrong character detected in the text detection subtask into the Bi-LSTM model, and output the optimal corrected character.…”

Section: Introductionmentioning

confidence: 99%

Post Text Processing of Chinese Speech Recognition Based on Bidirectional LSTM Networks and CRF

et al. 2019

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With the rapid development of Internet of Things Technology, speech recognition has been applied more and more widely. Chinese Speech Recognition is a complex process. In the process of speech-to-text conversion, due to the influence of dialect, environmental noise, and context, the accuracy of speech-to-text in multi-round dialogues and specific contexts is still not high. After the general speech recognition technology, the text after speech recognition can be detected and corrected in the specific context, which is helpful to improve the robustness of text comprehension and is a beneficial supplement to the speech recognition technology. In this paper, a text processing model after Chinese Speech Recognition is proposed, which combines a bidirectional long short-term memory (LSTM) network with a conditional random field (CRF) model. The task is divided into two stages: text error detection and text error correction. In this paper, a bidirectional long short-term memory (Bi-LSTM) network and conditional random field are used in two stages of text error detection and text error correction respectively. Through verification and system test on the SIGHAN 2013 Chinese Spelling Check (CSC) dataset, the experimental results show that the model can effectively improve the accuracy of text after speech recognition.

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Annotating the Literature with Disease Ontology

Cited by 7 publications

References 6 publications

BERST: An Engine and Tool for Exploring Biomedical Entities and Relationships

BERST: An Engine and Tool for Exploring Biomedical Entities and Relationships

Sentiment Analysis for E-Commerce Product Reviews in Chinese Based on Sentiment Lexicon and Deep Learning

Post Text Processing of Chinese Speech Recognition Based on Bidirectional LSTM Networks and CRF

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