A Dictionary-Based Approach for Identifying Biomedical Concepts

Gong, Li; Yang, Renyou; Liu, Quan; Dong, Zhen; Hong, Chen; Yang, Geng

doi:10.1142/s021800141757004x

Cited by 7 publications

(13 citation statements)

References 27 publications

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“…For example, sentiment analysis of product reviews is used (Cho et al, 2014) to identify the positive/negative attitudes of consumers toward the products. In biomedical fields, researchers built a professional biomedical concept dictionary and then used a dictionary-based approach to identify the related concepts from a large amount of literature to save searching time and increase text classification efficiency (Gong et al, 2017). However, to the best of our knowledge, there are no dictionaries developed focusing on sustainability keywords and themes in fashion.…”

Section: Literature Reviewmentioning

confidence: 99%

Exploring Global Fashion Sustainability Practices through Dictionary-Based Text Mining

Zhao

2021

Clothing and Textiles Research Journal

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Nowadays, more fashion companies have started to adopt various sustainability practices and communicate these practices through their annual public CSR reports. In this study, we aim to provide a holistic perspective of fashion companies’ sustainable development and investigate the sustainability practices of global fashion companies. A total of 181 CSR reports from 29 fashion companies were collected. A Dictionary approach text classification method, combined with Latent Dirichlet Allocation (LDA), a computer-assisted topic modeling algorithm, was implemented to detect and summarize the themes and keywords of detailed practices disclosed in CSR reports. The findings identified 12 main sustainability practices themes based on the triple bottom line theory and the moral responsibility of corporate sustainability theory. In general, waste management and human rights are the most frequently mentioned themes. The findings also suggest that global fashion companies adopted different sustainability strategies based on their product categories and competitive advantages.

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Section: Literature Reviewmentioning

confidence: 99%

Exploring Global Fashion Sustainability Practices through Dictionary-Based Text Mining

Zhao

2021

Clothing and Textiles Research Journal

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“…GENIA corpus is extracted from the MEDLINE database with MEDLINE ID, title, and abstract encoded in an XMLdatabase. Aiming at the abovementioned approach, we focused on five categories of entities, namely, DNA, protein, RNA, cell-type, and cell-line using three popular measurements which are used the works [40]. e experimental results are illustrated in Table 1.…”

Section: Resultsmentioning

confidence: 99%

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

Gong

Zhang

Chen

et al. 2021

Security and Communication Networks

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Disease relevant entities are an important task in mining unstructured text data from the biomedical literature for achieving biomedical knowledge. Autism spectrum disorder (ASD) is a disease related to a neurological and developmental disorder characterized by deficits in communication and social interaction and by repetitive behaviour. However, this kind of disease remains unclear to date. In this study, it identifies entities associated with disease using the machine learning of a computational way from text data collection for molecular mechanisms related to ASD. Entities related to disease are extracted from the biomedical literature related to autism by using deep learning with bidirectional long short-term memory (BiLSTM) and conditional random field (CRF) model. Compared other previous works, the approach is promising for identifying entities related to disease. The proposed approach including five types of molecular entities is evaluated by GENIA corpus to obtain an F-score of 76.81%. The work has extracted 9146 proteins, 145 RNAs, 7680 DNAs, 1058 cell-types, and 981 cell-lines from the autism biomedical literature after removing repeated molecular entities. Finally, we perform GO and KEGG analyses of the test dataset. This study could serve as a reference for further studies on the etiology of disease on the basis of molecular mechanisms and provide a way to explore disease genetic information.

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“…The approach obtained the overall 95.0% F-measure aiming at the GENIA corpus. The implementation of the approach is shown in Figure 3 [24].…”

Section: Resultsmentioning

confidence: 99%

“…In this section, we introduce our previous work [24][25][26] which is published in the International Journal of Pattern Recognition and Artificial Intelligence. We first achieve experimental literature and build a concept dictionary based on the authoritative corpus.…”

Section: Dictionary-based Approach To Identify Biomedical Entitiesmentioning

confidence: 99%

“…These extracted biologically meaningful terms build concept dictionary for the Application of Biomedical Text Mining http://dx.doi.org/10.5772/intechopen.75924 biomedical text mining. For example, the concept taxonomy of GENIA 3.02 version contains some biologically relevant nominal categories as shown in Table 1 [24].…”

Section: Biomedical Dictionaries' Constructionmentioning

confidence: 99%

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Application of Biomedical Text Mining

Gong¹

2018

Artificial Intelligence - Emerging Trends and Applications

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With the enormous volume of biological literature, increasing growth phenomenon due to the high rate of new publications is one of the most common motivations for the biomedical text mining. Aiming at this massive literature to process, it could extract more biological information for mining biomedical knowledge. Using the information will help understand the mechanism of disease generation, promote the development of disease diagnosis technology, and promote the development of new drugs in the field of biomedical research. Based on the background, this chapter introduces the rise of biomedical text mining. Then, it describes the biomedical text-mining technology, namely natural language processing, including the several components. This chapter emphasizes the two aspects in biomedical text mining involving static biomedical information recognization and dynamic biomedical information extraction using instance analysis from our previous works. The aim is to provide a way to quickly understand biomedical text mining for some researchers.

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A Dictionary-Based Approach for Identifying Biomedical Concepts

Cited by 7 publications

References 27 publications

Exploring Global Fashion Sustainability Practices through Dictionary-Based Text Mining

Exploring Global Fashion Sustainability Practices through Dictionary-Based Text Mining

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

Application of Biomedical Text Mining

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