Home / Advanced Search

  • Title/Keywords

  • Author/Affliations

  • Journal

  • Article Type

  • Start Year

  • End Year

Update SearchingClear
  • Articles
  • Online
Search Results (11)
  • Open Access


    A Weakly-Supervised Method for Named Entity Recognition of Agricultural Knowledge Graph

    Ling Wang, Jingchi Jiang*, Jingwen Song, Jie Liu

    Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 833-848, 2023, DOI:10.32604/iasc.2023.036402

    Abstract It is significant for agricultural intelligent knowledge services using knowledge graph technology to integrate multi-source heterogeneous crop and pest data and fully mine the knowledge hidden in the text. However, only some labeled data for agricultural knowledge graph domain training are available. Furthermore, labeling is costly due to the need for more data openness and standardization. This paper proposes a novel model using knowledge distillation for a weakly supervised entity recognition in ontology construction. Knowledge distillation between the target and source data domain is performed, where Bi-LSTM and CRF models are constructed for entity recognition. The experimental result is shown… More >

  • Open Access


    A Federated Named Entity Recognition Model with Explicit Relation for Power Grid

    Jingtang Luo1, Shiying Yao1, Changming Zhao2,*, Jie Xu3, Jim Feng4

    CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 4207-4216, 2023, DOI:10.32604/cmc.2023.034439

    Abstract The power grid operation process is complex, and many operation process data involve national security, business secrets, and user privacy. Meanwhile, labeled datasets may exist in many different operation platforms, but they cannot be directly shared since power grid data is highly privacy-sensitive. How to use these multi-source heterogeneous data as much as possible to build a power grid knowledge map under the premise of protecting privacy security has become an urgent problem in developing smart grid. Therefore, this paper proposes federated learning named entity recognition method for the power grid field, aiming to solve the problem of building a… More >

  • Open Access


    Data Masking for Chinese Electronic Medical Records with Named Entity Recognition

    Tianyu He1, Xiaolong Xu1,*, Zhichen Hu1, Qingzhan Zhao2, Jianguo Dai2, Fei Dai3

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3657-3673, 2023, DOI:10.32604/iasc.2023.036831

    Abstract With the rapid development of information technology, the electronification of medical records has gradually become a trend. In China, the population base is huge and the supporting medical institutions are numerous, so this reality drives the conversion of paper medical records to electronic medical records. Electronic medical records are the basis for establishing a smart hospital and an important guarantee for achieving medical intelligence, and the massive amount of electronic medical record data is also an important data set for conducting research in the medical field. However, electronic medical records contain a large amount of private patient information, which must… More >

  • Open Access


    Corpus of Carbonate Platforms with Lexical Annotations for Named Entity Recognition

    Zhichen Hu1, Huali Ren2, Jielin Jiang1, Yan Cui4, Xiumian Hu3, Xiaolong Xu1,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.135, No.1, pp. 91-108, 2023, DOI:10.32604/cmes.2022.022268

    Abstract An obviously challenging problem in named entity recognition is the construction of the kind data set of entities. Although some research has been conducted on entity database construction, the majority of them are directed at Wikipedia or the minority at structured entities such as people, locations and organizational nouns in the news. This paper focuses on the identification of scientific entities in carbonate platforms in English literature, using the example of carbonate platforms in sedimentology. Firstly, based on the fact that the reasons for writing literature in key disciplines are likely to be provided by multidisciplinary experts, this paper designs… More >

  • Open Access


    A Two-Phase Paradigm for Joint Entity-Relation Extraction

    Bin Ji1, Hao Xu1, Jie Yu1, Shasha Li1, Jun Ma1, Yuke Ji2,*, Huijun Liu1

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1303-1318, 2023, DOI:10.32604/cmc.2023.032168

    Abstract An exhaustive study has been conducted to investigate span-based models for the joint entity and relation extraction task. However, these models sample a large number of negative entities and negative relations during the model training, which are essential but result in grossly imbalanced data distributions and in turn cause suboptimal model performance. In order to address the above issues, we propose a two-phase paradigm for the span-based joint entity and relation extraction, which involves classifying the entities and relations in the first phase, and predicting the types of these entities and relations in the second phase. The two-phase paradigm enables… More >

  • Open Access


    Interpreting Randomly Wired Graph Models for Chinese NER

    Jie Chen1, Jiabao Xu1, Xuefeng Xi1,*, Zhiming Cui1, Victor S. Sheng2

    CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 747-761, 2023, DOI:10.32604/cmes.2022.020771

    Abstract Interpreting deep neural networks is of great importance to understand and verify deep models for natural language processing (NLP) tasks. However, most existing approaches only focus on improving the performance of models but ignore their interpretability. In this work, we propose a Randomly Wired Graph Neural Network (RWGNN) by using graph to model the structure of Neural Network, which could solve two major problems (word-boundary ambiguity and polysemy) of Chinese NER. Besides, we develop a pipeline to explain the RWGNN by using Saliency Map and Adversarial Attacks. Experimental results demonstrate that our approach can identify meaningful and reasonable interpretations for… More >

  • Open Access


    A Novel Named Entity Recognition Scheme for Steel E-Commerce Platforms Using a Lite BERT

    Maojian Chen1,2,3, Xiong Luo1,2,3,*, Hailun Shen4, Ziyang Huang4, Qiaojuan Peng1,2,3

    CMES-Computer Modeling in Engineering & Sciences, Vol.129, No.1, pp. 47-63, 2021, DOI:10.32604/cmes.2021.017491

    Abstract In the era of big data, E-commerce plays an increasingly important role, and steel E-commerce certainly occupies a positive position. However, it is very difficult to choose satisfactory steel raw materials from diverse steel commodities online on steel E-commerce platforms in the purchase of staffs. In order to improve the efficiency of purchasers searching for commodities on the steel E-commerce platforms, we propose a novel deep learning-based loss function for named entity recognition (NER). Considering the impacts of small sample and imbalanced data, in our NER scheme, the focal loss, the label smoothing, and the cross entropy are incorporated into… More >

  • Open Access


    Short Text Entity Disambiguation Algorithm Based on Multi-Word Vector Ensemble

    Qin Zhang1, Xuyu Xiang1,*, Jiaohua Qin1, Yun Tan1, Qiang Liu1, Neal N. Xiong2

    Intelligent Automation & Soft Computing, Vol.30, No.1, pp. 227-241, 2021, DOI:10.32604/iasc.2021.017648

    Abstract With the rapid development of network media, the short text has become the main cover of information dissemination by quickly disseminating relevant entity information. However, the lack of context in the short text can easily lead to ambiguity, which will greatly reduce the efficiency of obtaining information and seriously affect the user’s experience, especially in the financial field. This paper proposed an entity disambiguation algorithm based on multi-word vector ensemble and decision to eliminate the ambiguity of entities and purify text information in information processing. First of all, we integrate a variety of unsupervised pre-trained word vector models as vector… More >

  • Open Access


    Number Entities Recognition in Multiple Rounds of Dialogue Systems

    Shan Zhang1, Bin Cao1, Yueshen Xu2,*, Jing Fan1

    CMES-Computer Modeling in Engineering & Sciences, Vol.127, No.1, pp. 309-323, 2021, DOI:10.32604/cmes.2021.014802

    Abstract As a representative technique in natural language processing (NLP), named entity recognition is used in many tasks, such as dialogue systems, machine translation and information extraction. In dialogue systems, there is a common case for named entity recognition, where a lot of entities are composed of numbers, and are segmented to be located in different places. For example, in multiple rounds of dialogue systems, a phone number is likely to be divided into several parts, because the phone number is usually long and is emphasized. In this paper, the entity consisting of numbers is named as number entity. The discontinuous… More >

  • Open Access


    Arabic Named Entity Recognition: A BERT-BGRU Approach

    Norah Alsaaran*, Maha Alrabiah

    CMC-Computers, Materials & Continua, Vol.68, No.1, pp. 471-485, 2021, DOI:10.32604/cmc.2021.016054

    Abstract Named Entity Recognition (NER) is one of the fundamental tasks in Natural Language Processing (NLP), which aims to locate, extract, and classify named entities into a predefined category such as person, organization and location. Most of the earlier research for identifying named entities relied on using handcrafted features and very large knowledge resources, which is time consuming and not adequate for resource-scarce languages such as Arabic. Recently, deep learning achieved state-of-the-art performance on many NLP tasks including NER without requiring hand-crafted features. In addition, transfer learning has also proven its efficiency in several NLP tasks by exploiting pretrained language models… More >

Displaying 1-10 on page 1 of 11. Per Page  

Share Link