Zhaojie Sun¹, Linlin Xing^1,*, Longbo Zhang¹, Hongzhen Cai², Maozu Guo³

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4325-4340, 2024, DOI:10.32604/cmc.2024.053588 - 12 September 2024

Abstract Extracting valuable information from biomedical texts is one of the current research hotspots of concern to a wide range of scholars. The biomedical corpus contains numerous complex long sentences and overlapping relational triples, making most generalized domain joint modeling methods difficult to apply effectively in this field. For a complex semantic environment in biomedical texts, in this paper, we propose a novel perspective to perform joint entity and relation extraction; existing studies divide the relation triples into several steps or modules. However, the three elements in the relation triples are interdependent and inseparable, so we… More >

Run Yang^1,2,3, Yanping Chen^1,2,3,*, Jiaxin Yan^1,2,3, Yongbin Qin^1,2,3

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 4129-4147, 2024, DOI:10.32604/cmc.2024.051675 - 12 September 2024

Abstract The relation is a semantic expression relevant to two named entities in a sentence. Since a sentence usually contains several named entities, it is essential to learn a structured sentence representation that encodes dependency information specific to the two named entities. In related work, graph convolutional neural networks are widely adopted to learn semantic dependencies, where a dependency tree initializes the adjacency matrix. However, this approach has two main issues. First, parsing a sentence heavily relies on external toolkits, which can be error-prone. Second, the dependency tree only encodes the syntactical structure of a sentence,… More >

Halil Ibrahim Okur^1,2,*, Kadir Tohma¹, Ahmet Sertbas²

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 2209-2228, 2024, DOI:10.32604/cmc.2024.050585 - 15 May 2024

Abstract Text classification, by automatically categorizing texts, is one of the foundational elements of natural language processing applications. This study investigates how text classification performance can be improved through the integration of entity-relation information obtained from the Wikidata (Wikipedia database) database and BERT-based pre-trained Named Entity Recognition (NER) models. Focusing on a significant challenge in the field of natural language processing (NLP), the research evaluates the potential of using entity and relational information to extract deeper meaning from texts. The adopted methodology encompasses a comprehensive approach that includes text preprocessing, entity detection, and the integration of… More >

Chuyuan Wei^*, Jinzhe Li, Zhiyuan Wang, Shanshan Wan, Maozu Guo

CMC-Computers, Materials & Continua, Vol.79, No.2, pp. 3299-3314, 2024, DOI:10.32604/cmc.2024.047811 - 15 May 2024

Abstract Deep neural network-based relational extraction research has made significant progress in recent years, and it provides data support for many natural language processing downstream tasks such as building knowledge graph, sentiment analysis and question-answering systems. However, previous studies ignored much unused structural information in sentences that could enhance the performance of the relation extraction task. Moreover, most existing dependency-based models utilize self-attention to distinguish the importance of context, which hardly deals with multiple-structure information. To efficiently leverage multiple structure information, this paper proposes a dynamic structure attention mechanism model based on textual structure information, which deeply… More >

Wei Liu, Meijuan Yin^*, Jialong Zhang, Lunchong Cui

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 975-997, 2024, DOI:10.32604/cmc.2023.046475 - 30 January 2024

Abstract The joint entity relation extraction model which integrates the semantic information of relation is favored by relevant researchers because of its effectiveness in solving the overlapping of entities, and the method of defining the semantic template of relation manually is particularly prominent in the extraction effect because it can obtain the deep semantic information of relation. However, this method has some problems, such as relying on expert experience and poor portability. Inspired by the rule-based entity relation extraction method, this paper proposes a joint entity relation extraction model based on a relation semantic template automatically… More >

Zhigang Zhao¹, Xiong Luo^1,2,3,*, Maojian Chen^1,2,3, Ling Ma¹

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 225-257, 2024, DOI:10.32604/cmes.2023.031513 - 30 December 2023

Abstract Knowledge graph (KG) serves as a specialized semantic network that encapsulates intricate relationships among real-world entities within a structured framework. This framework facilitates a transformation in information retrieval, transitioning it from mere string matching to far more sophisticated entity matching. In this transformative process, the advancement of artificial intelligence and intelligent information services is invigorated. Meanwhile, the role of machine learning method in the construction of KG is important, and these techniques have already achieved initial success. This article embarks on a comprehensive journey through the last strides in the field of KG via machine More >

Yuexin Huang^1,2, Suihuai Yu¹, Jianjie Chu^1,*, Zhaojing Su^1,3, Yangfan Cong¹, Hanyu Wang¹, Hao Fan⁴

CMES-Computer Modeling in Engineering & Sciences, Vol.138, No.1, pp. 167-200, 2024, DOI:10.32604/cmes.2023.028268 - 22 September 2023

Abstract The acquisition of valuable design knowledge from massive fragmentary data is challenging for designers in conceptual product design. This study proposes a novel method for acquiring design knowledge by combining deep learning with knowledge graph. Specifically, the design knowledge acquisition method utilises the knowledge extraction model to extract design-related entities and relations from fragmentary data, and further constructs the knowledge graph to support design knowledge acquisition for conceptual product design. Moreover, the knowledge extraction model introduces ALBERT to solve memory limitation and communication overhead in the entity extraction module, and uses multi-granularity information to overcome More >

Bin He, Hao Meng, Zhejin Zhang, Rui Liu, Ting Zhang^*

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.1, pp. 403-419, 2023, DOI:10.32604/cmes.2023.023242 - 05 January 2023

Abstract A qualia role-based entity-dependency graph (EDG) is proposed to represent and extract quantity relations for solving algebra story problems stated in Chinese. Traditional neural solvers use end-to-end models to translate problem texts into math expressions, which lack quantity relation acquisition in sophisticated scenarios. To address the problem, the proposed method leverages EDG to represent quantity relations hidden in qualia roles of math objects. Algorithms were designed for EDG generation and quantity relation extraction for solving algebra story problems. Experimental result shows that the proposed method achieved an average accuracy of 82.2% on quantity relation extraction More >

Bin Ji¹, Hao Xu¹, Jie Yu¹, Shasha Li¹, Jun Ma¹, Yuke Ji^2,*, Huijun Liu¹

CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 1303-1318, 2023, DOI:10.32604/cmc.2023.032168 - 22 September 2022

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… More >

Huiyu Sun^*, Ralph Grishman

Intelligent Automation & Soft Computing, Vol.34, No.3, pp. 1415-1423, 2022, DOI:10.32604/iasc.2022.030794 - 25 May 2022

Abstract Active learning methods which present selected examples from the corpus for annotation provide more efficient learning of supervised relation extraction models, but they leave the developer in the unenviable role of a passive informant. To restore the developer’s proper role as a partner with the system, we must give the developer an ability to inspect the extraction model during development. We propose to make this possible through a representation based on lexicalized dependency paths (LDPs) coupled with an active learner for LDPs. We apply LDPs to both simulated and real active learning with ACE as evaluation More >