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  • Open Access

    ARTICLE

    Relation-Aware Entity Matching Using Sentence-BERT

    Huchen Zhou1, Wenfeng Huang1, Mohan Li1,*, Yulin Lai2

    CMC-Computers, Materials & Continua, Vol.71, No.1, pp. 1581-1595, 2022, DOI:10.32604/cmc.2022.020695

    Abstract A key aspect of Knowledge fusion is Entity Matching. The objective of this study was to investigate how to identify heterogeneous expressions of the same real-world entity. In recent years, some representative works have used deep learning methods for entity matching, and these methods have achieved good results. However, the common limitation of these methods is that they assume that different attribute columns of the same entity are independent, and inputting the model in the form of paired entity records will cause repeated calculations. In fact, there are often potential relations between different attribute columns of different entities. These relations… More >

  • Open Access

    ARTICLE

    BERT for Conversational Question Answering Systems Using Semantic Similarity Estimation

    Abdulaziz Al-Besher1, Kailash Kumar1, M. Sangeetha2,*, Tinashe Butsa3

    CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 4763-4780, 2022, DOI:10.32604/cmc.2022.021033

    Abstract Most of the questions from users lack the context needed to thoroughly understand the problem at hand, thus making the questions impossible to answer. Semantic Similarity Estimation is based on relating user’s questions to the context from previous Conversational Search Systems (CSS) to provide answers without requesting the user's context. It imposes constraints on the time needed to produce an answer for the user. The proposed model enables the use of contextual data associated with previous Conversational Searches (CS). While receiving a question in a new conversational search, the model determines the question that refers to more past CS. The… More >

  • Open Access

    ARTICLE

    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

    ARTICLE

    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

    ARTICLE

    Classification of Epileptic Electroencephalograms Using Time-Frequency and Back Propagation Methods

    Sengul Bayrak1,2,*, Eylem Yucel2, Hidayet Takci3, Ruya Samli2

    CMC-Computers, Materials & Continua, Vol.69, No.2, pp. 1427-1446, 2021, DOI:10.32604/cmc.2021.015524

    Abstract Today, electroencephalography is used to measure brain activity by creating signals that are viewed on a monitor. These signals are frequently used to obtain information about brain neurons and may detect disorders that affect the brain, such as epilepsy. Electroencephalogram (EEG) signals are however prone to artefacts. These artefacts must be removed to obtain accurate and meaningful signals. Currently, computer-aided systems have been used for this purpose. These systems provide high computing power, problem-specific development, and other advantages. In this study, a new clinical decision support system was developed for individuals to detect epileptic seizures using EEG signals. Comprehensive classification… More >

  • Open Access

    ARTICLE

    Parametric Methods for the Regional Assessment of Cardiac Wall Motion Abnormalities: Comparison Study

    Narjes Benameur1,*, Mazin Abed Mohammed2, Ramzi Mahmoudi3,4, Younes Arous5, Begonya Garcia-Zapirain6, Karrar Hameed Abdulkareem7, Mohamed Hedi Bedoui3

    CMC-Computers, Materials & Continua, Vol.69, No.1, pp. 1233-1252, 2021, DOI:10.32604/cmc.2021.016860

    Abstract Left ventricular (LV) dysfunction is mainly assessed by global contractile indices such as ejection fraction and LV Volumes in cardiac MRI. While these indices give information about the presence or not of LV alteration, they are not able to identify the location and the size of such alteration. The aim of this study is to compare the performance of three parametric imaging techniques used in cardiac MRI for the regional quantification of cardiac dysfunction. The proposed approaches were evaluated on 20 patients with myocardial infarction and 20 subjects with normal function. Three parametric images approaches: covariance analysis, parametric images based… More >

  • Open Access

    ARTICLE

    Condition Evaluation in Steel Truss Bridge with Fused Hilbert Transform, Spectral Kurtosis, and Bandpass Filter

    Anshul Sharma1,*, Pardeep Kumar1, Hemant Kumar Vinayak2, Suresh Kumar Walia3

    Structural Durability & Health Monitoring, Vol.15, No.2, pp. 139-165, 2021, DOI:10.32604/sdhm.2021.012316

    Abstract This study is concerned with the diagnosis of discrepancies in a steel truss bridge by identifying dynamic properties from the vibration response signals of the bridges. The vibration response signals collected at bridges under three different vehicular speeds of 10 km/hr, 20 km/hr, and 30 km/hr are analyzed using statistical features such as kurtosis, magnitude of peak-to-peak, root mean square, crest factor as well as impulse factor in time domain, and Stockwell transform in the time-frequency domain. The considered statistical features except for kurtosis show uncertain behavior. The Stockwell transform showed low-resolution outcomes when the presence of noise in the… More >

  • Open Access

    ARTICLE

    Phase Error Compensation of Three-Dimensional Reconstruction Combined with Hilbert Transform

    Tao Zhang1,*, Jie Shen1, Shaoen Wu2

    CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 3121-3131, 2021, DOI:10.32604/cmc.2021.016362

    Abstract Nonlinear response is an important factor affecting the accuracy of three-dimensional image measurement based on the fringe structured light method. A phase compensation algorithm combined with a Hilbert transform is proposed to reduce the phase error caused by the nonlinear response of a digital projector in the three-dimensional measurement system of fringe structured light. According to the analysis of the influence of Gamma distortion on the phase calculation, the algorithm establishes the relationship model between phase error and harmonic coefficient, introduces phase shift to the signal, and keeps the signal amplitude constant while filtering out the DC component. The phase… More >

  • Open Access

    ARTICLE

    Leverage External Knowledge and Self-attention for Chinese Semantic Dependency Graph Parsing

    Dianqing Liu1,2, Lanqiu Zhang1,2, Yanqiu Shao1,2,*, Junzhao Sun3

    Intelligent Automation & Soft Computing, Vol.28, No.2, pp. 447-458, 2021, DOI:10.32604/iasc.2021.016320

    Abstract Chinese semantic dependency graph (CSDG) parsing aims to analyze the semantic relationship between words in a sentence. Since it is a deep semantic analysis task, the parser needs a lot of prior knowledge about the real world to distinguish different semantic roles and determine the range of the head nodes of each word. Existing CSDG parsers usually use part-of-speech (POS) and lexical features, which can only provide linguistic knowledge, but not semantic knowledge about the word. To solve this problem, we propose an entity recognition method based on distant supervision and entity classification to recognize entities in sentences, and then… More >

  • Open Access

    ARTICLE

    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 >

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