Neural relationship extraction is an important task in natural language processing, aimed at extracting relationships between target entity pairs from a given text. In recent years, with the development of deep neural networks, various types of neural networks to extract sentence entity-level, fragment-level, and sentence-level features for relationship extraction have become a mainstream research direction. Most existing studies use the BERT model to embed sentences and then use CNN to manipulate all words in the entire sentence to obtain fragment-level features. This article proposes a new word-centered context fragment-level method based on pruning the shortest dependency path between entity pairs. We demonstrate that using a pruning method based on the shortest dependency path between entity pairs can effectively improve the ability of model fragments and information extraction. We evaluated our method on a public benchmark: SemEval 2010 Task 8. The experimental results show that our method outperforms the advanced model using BERT as the embedding.
This study introduces a framework for clustering competition coevolution optimization algorithm based on the parallel Lion Swarm Optimization Algorithm (LSO). This framework combines clustering and competitive coevolution concepts under existing parallel computing paradigms. Initially, clustering categorizes particles of the total population, followed by parallel computing principles where particles within each classified subpopulation undergo local optimization using distinct optimization mechanisms. After a certain number of iterations, these subpopulations coevolve through an island-based topology. Experimental results demonstrate significant advantages of the proposed algorithm over traditional methods in both CEC2013 benchmark functions and feature selection problems, affirming its potential and effectiveness in practical applications. This framework introduces a novel approach and method for addressing complex problems, offering broad prospects for application.
A group decision making approach considering the correlation of multiple evaluation attributes under information described by Z-numbers is proposed. The approach primarily consists of two parts: (1) obtaining the group preference matrix under information described by Z-numbers; (2) addressing the interactions among multiple evaluation attributes. To achieve this, firstly, a technique for ranking Z-numbers is proposed. The provided ranking technique better distinguishes Z-numbers. Additionally, the Choquet integral for Z-numbers (ZN − CIβ) is introduced. The ZN − CIβ captures the interconnections among multiple evaluation attributes in an uncertain environment. Secondly, the weights of decision makers are determined through Shapley values, ensuring fairness in the decision making process. In addition, the comprehensive group decision matrix is formed by aggregating the matrices of individual decision makers, each represented by their respective Z-numbers. Finally, the efficacy of the approach presented in this paper is validated through a case of selecting B&Bs.
Determining Hopf bifurcation points is an important task in the study of nonlinear dynamic systems, but existing methods require a large amount of computation and have low ratios of success. In this study, we present a numerical method to determine Hopf bifurcation points: the Split Iteration Technique and prove its convergence. Compared with existing methods, this method results in much less computation and a much higher ratio of success.
The study proposes a novel approach that uses block pulse functions (BPFs) with constant delays to approximate stochastic Volterra integral equations (SVIEs). The method simplifies the problem by converting delay-containing SVIEs into algebraic ones using operational matrices of BPFs. This approach is easily solvable and effective, as shown through numerical examples.
