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  • Open Access

    ARTICLE

    RoBGP: A Chinese Nested Biomedical Named Entity Recognition Model Based on RoBERTa and Global Pointer

    Xiaohui Cui1,2,#, Chao Song1,2,#, Dongmei Li1,2,*, Xiaolong Qu1,2, Jiao Long1,2, Yu Yang1,2, Hanchao Zhang3

    CMC-Computers, Materials & Continua, Vol.78, No.3, pp. 3603-3618, 2024, DOI:10.32604/cmc.2024.047321

    Abstract Named Entity Recognition (NER) stands as a fundamental task within the field of biomedical text mining, aiming to extract specific types of entities such as genes, proteins, and diseases from complex biomedical texts and categorize them into predefined entity types. This process can provide basic support for the automatic construction of knowledge bases. In contrast to general texts, biomedical texts frequently contain numerous nested entities and local dependencies among these entities, presenting significant challenges to prevailing NER models. To address these issues, we propose a novel Chinese nested biomedical NER model based on RoBERTa and Global Pointer (RoBGP). Our model… More >

  • Open Access

    ARTICLE

    The Entity Relationship Extraction Method Using Improved RoBERTa and Multi-Task Learning

    Chaoyu Fan*

    CMC-Computers, Materials & Continua, Vol.77, No.2, pp. 1719-1738, 2023, DOI:10.32604/cmc.2023.041395

    Abstract There is a growing amount of data uploaded to the internet every day and it is important to understand the volume of those data to find a better scheme to process them. However, the volume of internet data is beyond the processing capabilities of the current internet infrastructure. Therefore, engineering works using technology to organize and analyze information and extract useful information are interesting in both industry and academia. The goal of this paper is to explore the entity relationship based on deep learning, introduce semantic knowledge by using the prepared language model, develop an advanced entity relationship information extraction… More >

  • Open Access

    ARTICLE

    Chinese Cyber Threat Intelligence Named Entity Recognition via RoBERTa-wwm-RDCNN-CRF

    Zhen Zhen1, Jian Gao1,2,*

    CMC-Computers, Materials & Continua, Vol.77, No.1, pp. 299-323, 2023, DOI:10.32604/cmc.2023.042090

    Abstract In recent years, cyber attacks have been intensifying and causing great harm to individuals, companies, and countries. The mining of cyber threat intelligence (CTI) can facilitate intelligence integration and serve well in combating cyber attacks. Named Entity Recognition (NER), as a crucial component of text mining, can structure complex CTI text and aid cybersecurity professionals in effectively countering threats. However, current CTI NER research has mainly focused on studying English CTI. In the limited studies conducted on Chinese text, existing models have shown poor performance. To fully utilize the power of Chinese pre-trained language models (PLMs) and conquer the problem… More >

  • Open Access

    ARTICLE

    Leveraging Readability and Sentiment in Spam Review Filtering Using Transformer Models

    Sujithra Kanmani*, Surendiran Balasubramanian

    Computer Systems Science and Engineering, Vol.45, No.2, pp. 1439-1454, 2023, DOI:10.32604/csse.2023.029953

    Abstract Online reviews significantly influence decision-making in many aspects of society. The integrity of internet evaluations is crucial for both consumers and vendors. This concern necessitates the development of effective fake review detection techniques. The goal of this study is to identify fraudulent text reviews. A comparison is made on shill reviews vs. genuine reviews over sentiment and readability features using semi-supervised language processing methods with a labeled and balanced Deceptive Opinion dataset. We analyze textual features accessible in internet reviews by merging sentiment mining approaches with readability. Overall, the research improves fake review screening by using various transformer models such… More >

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