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一种易用的实体识别消歧系统评测框架 Article

辉陈,宝刚魏,一鸣李,Yong-huai LIU,文浩朱

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 2, Pages 195-205 doi: 10.1631/FITEE.1500473

Abstract: 实体识别消歧是知识库扩充和信息抽取的重要技术之一。近些年该领域诞生了很多研究成果，提出了许多实体识别消歧系统。但由于缺乏对这些系统的完善评测对比，该领域依然处于良莠淆杂的状态。本文提出一个实体识别消歧系统的统一评测框架，用于公平地比较各个实体识别消歧系统的效果。该框架代码开源，可以采用新的系统、数据集、评测机制扩展。通过该框架评测实体系统，可以分析得到系统各个模块的优劣之处。本文分析对比了几个公开的实体识别消歧系统，并总结出了一些有用的结论。

Keywords：实体识别消歧；评测框架；信息抽取

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Disambiguating named entitieswith deep supervised learning via crowd labels Article

Le-kui ZHOU,Si-liang TANG,Jun XIAO,Fei WU,Yue-ting ZHUANG

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 1, Pages 97-106 doi: 10.1631/FITEE.1601835

Abstract: Named entity disambiguation (NED) is the task of linking mentions of ambiguous entities to their referenced entities in a knowledge base such as Wikipedia. We propose an approach to effectively disentangle the discriminative features in the manner of collaborative utilization of collective wisdom (via human-labeled crowd labels) and deep learning (via human-generated data) for the NED task. In particular, we devise a crowd model to elicit the underlying features (crowd features) from crowd labels that indicate a matching candidate for each mention, and then use the crowd features to fine-tune a dynamic convolutional neural network (DCNN). The learned DCNN is employed to obtain deep crowd features to enhance traditional hand-crafted features for the NED task. The proposed method substantially benefits from the utilization of crowd knowledge (via crowd labels) into a generic deep learning for the NED task. Experimental analysis demonstrates that the proposed approach is superior to the traditional hand-crafted features when enough crowd labels are gathered.

Keywords： Named entity disambiguation Crowdsourcing Deep learning

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A review on cyber security named entity recognition Review Article

Chen Gao, Xuan Zhang, Mengting Han, Hui Liu,zhxuan@ynu.edu.cn

Frontiers of Information Technology & Electronic Engineering 2021, Volume 22, Issue 9, Pages 1153-1168 doi: 10.1631/FITEE.2000286

Abstract: With the rapid development of Internet technology and the advent of the era of big data, more and more texts are provided on the Internet. These texts include not only security concepts, incidents, tools, guidelines, and policies, but also risk management approaches, best practices, assurances, technologies, and more. Through the integration of large-scale, heterogeneous, unstructured information, the identification and classification of entities can help handle issues. Due to the complexity and diversity of texts in the domain, it is difficult to identify security entities in the domain using the traditional methods. This paper describes various approaches and techniques for NER in this domain, including the rule-based approach, dictionary-based approach, and based approach, and discusses the problems faced by NER research in this domain, such as conjunction and disjunction, non-standardized naming convention, abbreviation, and massive nesting. Three future directions of NER in are proposed: (1) application of unsupervised or semi-supervised technology; (2) development of a more comprehensive ontology; (3) development of a more comprehensive model.

Keywords：命名实体识别（NER）；信息抽取；网络空间安全；机器学习；深度学习

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A network security entity recognition method based on feature template and CNN-BiLSTM-CRF Research Papers

Ya QIN, Guo-wei SHEN, Wen-bo ZHAO, Yan-ping CHEN, Miao YU, Xin JIN

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 6, Pages 872-884 doi: 10.1631/FITEE.1800520

Abstract:

By network security threat intelligence analysis based on a security knowledge graph (SKG), multi-source threat intelligence data can be analyzed in a fine-grained manner. This has received extensive attention. It is difficult for traditional named entity recognition methods to identify mixed security entities in Chinese and English in the field of network security, and there are difficulties in accurately identifying network security entities because of insufficient features extracted. In this paper, we propose a novel FT-CNN-BiLSTM-CRF security entity recognition method based on a neural network CNN-BiLSTM-CRF model combined with a feature template (FT). The feature template is used to extract local context features, and a neural network model is used to automatically extract character features and text global features. Experimental results showed that our method can achieve an F-score of 86% on a large-scale network security dataset and outperforms other methods.

Keywords： Network security entity Security knowledge graph (SKG) Entity recognition Feature template Neural network

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Learning to select pseudo labels: a semi-supervised method for named entity recognition Research Articles

Zhen-zhen Li, Da-wei Feng, Dong-sheng Li, Xi-cheng Lu,lizhenzhen14@nudt.edu.cn,davyfeng.c@gmail.com,dsli@nudt.edu.cn,xclu@nudt.edu.cn

Frontiers of Information Technology & Electronic Engineering 2020, Volume 21, Issue 6, Pages 809-962 doi: 10.1631/FITEE.1800743

Abstract: models have achieved state-of-the-art performance in (NER); the good performance, however, relies heavily on substantial amounts of labeled data. In some specific areas such as medical, financial, and military domains, labeled data is very scarce, while is readily available. Previous studies have used to enrich word representations, but a large amount of entity information in is neglected, which may be beneficial to the NER task. In this study, we propose a for NER tasks, which learns to create high-quality labeled data by applying a pre-trained module to filter out erroneous pseudo labels. Pseudo labels are automatically generated for and used as if they were true labels. Our semi-supervised framework includes three steps: constructing an optimal single neural model for a specific NER task, learning a module that evaluates pseudo labels, and creating new labeled data and improving the NER model iteratively. Experimental results on two English NER tasks and one Chinese clinical NER task demonstrate that our method further improves the performance of the best single neural model. Even when we use only pre-trained static word embeddings and do not rely on any external knowledge, our method achieves comparable performance to those state-of-the-art models on the CoNLL-2003 and OntoNotes 5.0 English NER tasks.

Keywords：命名实体识别；无标注数据；深度学习；半监督学习方法

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Both-Branch Fuzzy Decision and Problems on Decision Discernment

Shi Kaiquan,Li Qiqiang

Strategic Study of CAE 2001, Volume 3, Issue 1, Pages 71-77

Abstract:

This paper proposes the concept of both-branch fuzzy decision on X, which contains neutral universe(X^*≠{x}), and the optimal decision analysis model. In addition, the paper proposes decision judgement theorem, decision discernment theorem, decision surplusage-discarding theorem and hole-digging principle on decision factors universe X. Both-branch fuzzy decision has such characteristics as decision two-direction dependence character, decision piling-synthesis character, decision branch-separating character, decision branch-degeneration character, and decision non-fault character. The research results have found application.

Keywords： both-branch fuzzy decision decision model judgement theorem discernment theorem surplusage-discarding theorem hole-digging principle

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DDUC: an erasure-coded system with decoupled data updating and coding Research Article

Xiang LI, Yibing LI, Chunrui TANG, Yingsong LI,chunruitang@126.com

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 5, Pages 742-758 doi: 10.1631/FITEE.2200253

Abstract: To improve the accuracy of modulated signal recognition in variable environments and reduce the impact of factors such as lack of prior knowledge on recognition results, researchers have gradually adopted deep learning techniques to replace traditional modulated signal processing techniques. To address the problem of low recognition accuracy of the modulated signal at low signal-to-noise ratios, we have designed a novel network of multi-scale analysis with deep threshold noise elimination to recognize the actually collected modulated signals under a symmetric cross-entropy function of label smoothing. The network consists of a denoising encoder with deep adaptive threshold learning and a decoder with . The two modules are skip-connected to work together to improve the robustness of the overall network. Experimental results show that this method has better recognition accuracy at low signal-to-noise ratios than previous methods. The network demonstrates a flexible self-learning capability for different noise thresholds and the effectiveness of the designed feature fusion module in multi-scale feature acquisition for various modulation types.

Keywords： Signal noise elimination Deep adaptive threshold learning network Multi-scale feature fusion Modulation recognition

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Improving entity linking with two adaptive features Research Article

Hongbin ZHANG, Quan CHEN, Weiwen ZHANG

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 11, Pages 1620-1630 doi: 10.1631/FITEE.2100495

Abstract:

(EL) is a fundamental task in natural language processing. Based on neural networks, existing systems pay more attention to the construction of the , but ignore latent semantic information in the and the acquisition of effective information. In this paper, we propose two , in which the first adaptive feature enables the local and s to capture latent information, and the second adaptive feature describes effective information for embeddings. These can work together naturally to handle some uncertain information for EL. Experimental results demonstrate that our EL system achieves the best performance on the AIDA-B and MSNBC datasets, and the best average performance on out-domain datasets. These results indicate that the proposed , which are based on their own diverse contexts, can capture information that is conducive for EL.

Keywords： Entity linking Local model Global model Adaptive features Entity type

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Research on evaluation of extinguishing concentration of superfine ammonium phosphate extinguishing agent by laser attenuation measurement

Yin Zhiping,Liu Aihua,Pan Renming

Strategic Study of CAE 2008, Volume 10, Issue 7, Pages 90-95

Abstract:

Extinguishing concentration measurement experiments in the cup burner on two kinds of superfine ammonium phosphate extinguishing agents with different particle sizes had been done by means of filtering and weighing method combined with laser attenuation measurement. The fitting curves of particle concentration with laser attenuation rate and the extinguishing concentration of superfine particle agents had been got. The extinguishing mass concentration of two super fine agents were measured in cup burner. The results showed that when the median particle size of two kinds of extinguishing agents were 6.0 μm and 13.7μm, their laser absorption coefficients were 0.353 m²/g and 0.257 3 m²/g respectively and their mean extinguishing concentration were 32.9 g/m³ and 41.6 g/m³respectively. Extinguishing ability of the former was 25 % to 30 % higher than that of the latter. The laser attenuation measurement relative errors were less when the former agent was used in experiment.

Keywords： laser attenuation measurement filtering and weighing method superfine particle extinguishing agent extinguishing concentration

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A decision-making method about the design quality of component-based active load section entity model for protective engineering

Yuan Hui,Wang Fengshan,Xu Jiheng,Fu Chengqun

Strategic Study of CAE 2013, Volume 15, Issue 5, Pages 106-112

Abstract:

To effectively express the protective engineering space object, and effectively support various topology operation and military damage applications, a component-based entity model design scheme and its quality was proposed for the active load section of protective engineering. According to the design variety and validity confirmation in component-based protective engineering entity model, the positive and negative ideal intuitionistic fuzzy design project was determined, and respectively comparing the distance from the design project to the positive and negative ideal project, the superiority degree model was established for the component-based entity model design projects, which further gained the sequence of such projects. Case showed that model effectively solved the decision-making problem about entity model design operations, which provided one theory and method for scientific decision-making practice in entity model design operation for such active load section of protective engineering.

Keywords： protective engineering component design quality entity model intuitionistic fuzzy sets superiority

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A Parallel Evolutionary Algorithm Based on Space Contraction

Wang Tao,LiQiqiang

Strategic Study of CAE 2003, Volume 5, Issue 3, Pages 57-61

Abstract:

A novel algorithm which is based on space contraction for solving MINLP problems is proposed. The algorithm applies fast and effective non-complete evolution to the search for the information of better solutions, by which locates the possible area of optimal solutions, determines next search space by the information of elite individuals. The result shows that it is better than other existing evolutionary algorithms in search efficiency, range of applications, accuracy and robustness of solutions.

Keywords： space contraction evolutionary algorithms MINLP

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A robust optimization model considering probability distribution

Ding Ran,Li Qiqiang,Zhang Yuanpeng

Strategic Study of CAE 2008, Volume 10, Issue 9, Pages 70-73

Abstract:

Robust optimization is a method to process optimization problem under uncertainty. The current robust optimization methods have some deficiencies in application conditions and probability utilization. Based on the chance constraints programming, two kinds of robust constraints according to two different kinds of probability distribution of the stochastic parameters are proposed, and a novel robust optimization model is proposed. The feasible solutions of this model can be controlled to satisfy the robust index. This model can be used in the situations that both sides of the constraints contain stochastic parameters, and can be easily extended to non-liner models. The simulation results illustrate the validity of the model.

Keywords： uncertainty robust optimization stochastic programming chance constraints

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Fuzzy Modeling Theory on Production Scheduling： A Survey

Zhang Hong,Li qiqiang,Guo qingqiang,Zhang Peng,Gao Yuan

Strategic Study of CAE 2005, Volume 7, Issue 12, Pages 92-102

Abstract:

A brief survey on classical modeling theory of production scheduling is presented in this paper. With the combination of fuzzy mathematical theory and classical modeling or intelligent methods, a brief survey on fuzzy modeling theory is also presented. Some perspective viewpoints are pointed out in the last section of the paper.

Keywords： production scheduling fuzzy mathematics fuzzy modeling

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Study of Stability of Production Scheduling

Li Qiqiang,Shi Kaiquan

Strategic Study of CAE 2001, Volume 3, Issue 3, Pages 75-79

Abstract:

A production scheduling solution involves a great deal of constrains, which determine the feasibility of the solution. So the study of the stability orientated to constrains is the focus of this paper. The definitions of the satisfaction degree of hard constrains and soft constrains are presented, and a concept of stable degree of production scheduling is proposed. A simulation result demonstrates the significance of the stability degree of scheduling in practical production processes.

Keywords： production scheduling constrains satisfaction degree scheduling stability

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Survey on Particle Swarm Optimization Algorithm

Yang Wei,Li Chiqiang

Strategic Study of CAE 2004, Volume 6, Issue 5, Pages 87-94

Abstract:

Particle swarm optimization (PSO) is a new optimization technique originating from artificial life and evolutionary computation. The algorithm completes the optimization through following the personal best solution of each particle and the global best value of the whole swarm. PSO can be implemented with ease and few parameters need to be tuned. It has been successfully applied in many areas. In this paper, the basic principles of PSO are introduced at length, and various improvements and applications of PSO are also presented. Finally, some future research directions about PSO are proposed.

Keywords： swarm intelligence evolutionary algorithm particle swarm optimization