检索结果-南通市图书馆

Decoupled Two-Phase Framework for Class-Incremental Few-Shot named entity recognition

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Tsinghua Science and Technology 2023年第5期28卷 976-987页

作者： Yifan Chen Zhen Huang Minghao Hu Dongsheng Li Changjian Wang Feng Liu Xicheng Lu College of Computer National University of Defense TechnologyChangsha 410073China Information Research Center of Military Science PLA Academy of Military ScienceBeijing 100091China

Class-Incremental Few-Shot named entity recognition(CIFNER)aims to identify entity categories that have appeared with only a few newly added(novel)class ***,existing class-incremental methods typically introduce new parameters to adapt to new classes and treat all information equally,resulting in poor ***,few-shot methods necessitate samples for all observed classes,making them difficult to transfer into a class-incremental ***,a decoupled two-phase framework method for the CIFNER task is proposed to address the above *** whole task is converted to two separate tasks named entity Span Detection(ESD)and entity Class Discrimination(ECD)that leverage parameter-cloning and label-fusion to learn different levels of knowledge separately,such as class-generic knowledge and class-specific ***,different variants,such as the Conditional Random Field-based(CRF-based),word-pair-based methods in ESD module,and add-based,Natural Language Inference-based(NLI-based)and prompt-based methods in ECD module,are investigated to demonstrate the generalizability of the decoupled *** experiments on the three named entity recognition(NER)datasets reveal that our method achieves the state-of-the-art performance in the CIFNER setting.

关键词： named entity recognition deep learning class-incremental learning few-shot learning

Hierarchical LSTM with char-subword-word tree-structure representation for Chinese named entity recognition

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Science China(Information Sciences) 2020年第10期63卷 74-88页

作者： Chen GONG Zhenghua LI Qingrong XIA Wenliang CHEN Min ZHANG School of Computer Science and Technology Soochow University

Chinese named entity recognition(CNER) aims to identify entity names such as person names and organization names from Chinese raw text and thus can quickly extract the entity information that people are concerned about from large-scale texts. Recent studies attempt to improve performance by integrating lexicon words into char-based CNER models. These existing studies, however, usually focus on leveraging the context-free words in lexicon without considering the contextual information of words and subwords in the sentences. To address this issue, in addition to utilizing the lexicon words, we further propose to construct a hierarchical tree structure representation composed of characters, subwords and context-aware predicted words from segmentor to represent each sentence for CNER. Based on the tree-structure representation, we propose a hierarchical long short-term memory(HiLSTM) framework, which consists of hierarchical encoding layer, fusion layer and CRF layer, to capture linguistic knowledge at different levels. On the one hand, the interactions within each level help to obtain the contextual information. On the other hand, the propagations from the lower-levels to the upper-levels can provide additional semantic knowledge for CNER. Experimental results on three widely used CNER datasets show that our proposed HiLSTM model achieves significant improvement over several strong benchmark methods.

关键词： natural language processing named entity recognition representation learning neural networks

Adversarial Active Learning for named entity recognition in Cybersecurity

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2021年第1期66卷 407-420页

作者： Tao Li Yongjin Hu Ankang Ju Zhuoran Hu Zhengzhou Institute of Information Science and Technology Zhengzhou450001China College of Letters and Science University of Wisconsin-MadisonMadison53706USA

Owing to the continuous barrage of cyber threats,there is a massive amount of cyber threat ***,a great deal of cyber threat intelligence come from textual *** analysis of cyber threat intelligence,many security analysts rely on cumbersome and time-consuming manual *** knowledge graph plays a significant role in automatics analysis of cyber threat *** the foundation for constructing cybersecurity knowledge graph,named entity recognition(NER)is required for identifying critical threat-related elements from textual cyber threat ***,deep neural network-based models have attained very good results in ***,the performance of these models relies heavily on the amount of labeled *** labeled data in cybersecurity is scarce,in this paper,we propose an adversarial active learning framework to effectively select the informative samples for further *** addition,leveraging the long short-term memory(LSTM)network and the bidirectional LSTM(BiLSTM)network,we propose a novel NER model by introducing a dynamic attention mechanism into the BiLSTM-LSTM *** the selected informative samples annotated,the proposed NER model is *** a result,the performance of the NER model is incrementally enhanced with low labeling *** results show the effectiveness of the proposed method.

关键词： Adversarial learning active learning named entity recognition dynamic attention mechanism

Integrated Chinese Segmentation, Parsing and named entity recognition

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Chinese Journal of Electronics 2018年第4期27卷 756-760页

作者： LI Dongchen ZHANG Xiantao WU Xihong Key Laboratory of Machine Perception and Intelligence Speech and Hearing Research Center Peking University

Segmentation, named entity recognition and parsing are standalone techniques in natural language processing community, and their annotations are inconsistent. However, the joint output is needed in some practical use, and they rely on the result of each other to make more concise output. A unified model is learned to resolve these three tasks simultaneously. At the training stage, the joint annotation of the three tasks are employed to learn a unified model. At the decoding stage, the three tasks are carried out on a given text to provide a consistent *** results demonstrate the higher performance for each task and verify the benefits of the unified framework.

关键词： Parsing Joint representation named entity recognition

SciCN:A Scientific Dataset for Chinese named entity recognition

同方期刊数据库评论

在线全文

同方期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2024年第3期78卷 4303-4315页

作者： Jing Yang Bin Ji Shasha Li Jun Ma Jie Yu College of Computer National University of Defense TechnologyChangsha410073China

named entity recognition(NER)is a fundamental task of information extraction(IE),and it has attracted considerable research attention in recent *** abundant annotated English NER datasets have significantly promoted the NER research in the English *** contrast,much fewer efforts are made to the Chinese NER research,especially in the scientific domain,due to the scarcity of Chinese NER *** alleviate this problem,we present aChinese scientificNER dataset–SciCN,which contains entity annotations of titles and abstracts derived from 3,500 scientific *** manually annotate a total of 62,059 entities,and these entities are classified into six *** to English scientific NER datasets,SciCN has a larger scale and is more diverse,for it not only contains more paper abstracts but these abstracts are derived from more research *** investigate the properties of SciCN and provide baselines for future research,we adapt a number of previous state-of-theart Chinese NER models to evaluate *** results show that SciCN is more challenging than other Chinese NER *** addition,previous studies have proven the effectiveness of using lexicons to enhance Chinese NER *** by this fact,we provide a scientific domain-specific *** results demonstrate that our lexicon delivers better performance gains than lexicons of other *** hope that the SciCN dataset and the lexicon will enable us to benchmark the NER task regarding the Chinese scientific domain and make progress for future *** dataset and lexicon are available at:https://***/yangjingla/***.

关键词： named entity recognition dataset scientific information extraction lexicon

A Novel named entity recognition Scheme for Steel E-Commerce Platforms Using a Lite BERT

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computer Modeling in Engineering & Sciences 2021年第10期129卷 47-63页

作者： Maojian Chen Xiong Luo Hailun Shen Ziyang Huang Qiaojuan Peng School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Key Laboratory of Knowledge Engineering for Materials Science Beijing100083China Shunde Graduate School University of Science and Technology BeijingFoshan528399China Ouyeel Co. Ltd.Shanghai201999China

In the era of big data,E-commerce plays an increasingly important role,and steel E-commerce certainly occupies a positive ***,it is very difficult to choose satisfactory steel raw materials from diverse steel commodities online on steel E-commerce platforms in the purchase of *** 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 a lite bidirectional encoder representations from transformers(BERT)model to avoid the ***,through the analysis of different classic annotation techniques used to tag data,an ideal one is chosen for the training model in our proposed *** are conducted on Chinese steel E-commerce *** experimental results show that the training time of a lite BERT(ALBERT)-based method is much shorter than that of BERT-based models,while achieving the similar computational performance in terms of metrics precision,recall,and F1 with BERT-based ***,our proposed approach performs much better than that of combining Word2Vec,bidirectional long short-term memory(Bi-LSTM),and conditional random field(CRF)models,in consideration of training time and F1.

关键词： named entity recognition bidirectional encoder representations from transformers steel E-commerce platform annotation technique

A Federated named entity recognition Model with Explicit Relation for Power Grid

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2023年第5期75卷 4207-4216页

作者： Jingtang Luo Shiying Yao Changming Zhao Jie Xu Jim Feng State Grid Sichuan Economic Research Institute ChengduChina School of Computer Science Chengdu University of Information TechnologyChengduChina School of Information and Communication Engineering University of Electronic Science and Technology of China ChengduChina Amphenol Global Interconnect Systems San JoseCA 95131USA

The power grid operation process is complex,and many operation process data involve national security,business secrets,and user ***,labeled datasets may exist in many different operation platforms,but they cannot be directly shared since power grid data is highly *** 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 ***,this paper proposes federated learning named entity recognition method for the power grid field,aiming to solve the problem of building a named entity recognition model covering the entire power grid process training by data with different security *** decompose the named entity recognition(NER)model FLAT(Chinese NER Using Flat-Lattice Transformer)in each platform into a global part and a local *** local part is used to capture the characteristics of the local data in each platform and is updated using locally labeled *** global part is learned across different operation platforms to capture the shared NER *** local gradients fromdifferent platforms are aggregated to update the global model,which is further delivered to each platform to update their global *** on two publicly available Chinese datasets and one power grid dataset validate the effectiveness of our method.

关键词： Power grid named entity recognition federal learning

RoBGP:A Chinese Nested Biomedical named entity recognition Model Based on RoBERTa and Global Pointer

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2024年第3期78卷 3603-3618页

作者： Xiaohui Cui Chao Song Dongmei Li Xiaolong Qu Jiao Long Yu Yang Hanchao Zhang School of Information Science and Technology Beijing Forestry UniversityBeijing100083China Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland Administration Beijing100083China Division of Biostatistics Department of Population HealthGrossman School of MedicineNew York UniversityNew York10016USA

named entity recognition(NER)stands as a fundamental task within the field of biomedical text mining,aiming to extract specific types of entities such as genes,proteins,and diseases from complex biomedical texts and categorize them into predefined entity *** process can provide basic support for the automatic construction of knowledge *** contrast to general texts,biomedical texts frequently contain numerous nested entities and local dependencies among these entities,presenting significant challenges to prevailing NER *** address these issues,we propose a novel Chinese nested biomedical NER model based on RoBERTa and Global Pointer(RoBGP).Our model initially utilizes the RoBERTa-wwm-ext-large pretrained language model to dynamically generate word-level initial *** then incorporates a Bidirectional Long Short-Term Memory network for capturing bidirectional semantic information,effectively addressing the issue of long-distance ***,the Global Pointer model is employed to comprehensively recognize all nested entities in the *** conduct extensive experiments on the Chinese medical dataset CMeEE and the results demonstrate the superior performance of RoBGP over several baseline *** research confirms the effectiveness of RoBGP in Chinese biomedical NER,providing reliable technical support for biomedical information extraction and knowledge base construction.

关键词： Biomedicine knowledge base named entity recognition pretrained language model global pointer

Corpus of Carbonate Platforms with Lexical Annotations for named entity recognition

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Computer Modeling in Engineering & Sciences 2023年第4期135卷 91-108页

作者： Zhichen Hu Huali Ren Jielin Jiang Yan Cui Xiumian Hu Xiaolong Xu School of Computer and Software Nanjing University of Information Science and TechnologyNanjing210044China Institution of Artificial Intelligence and Blockchain Guangzhou UniversityGuangzhou515021China School of Earth Sciences and Engineering Nanjing UniversityNanjing210023China College of Mathematics and Information Science Nanjing Normal University of Special EducationNanjing210023China

An obviously challenging problem in named entity recognition is the construction of the kind data set of *** 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 *** paper focuses on the identification of scientific entities in carbonate platforms in English literature,using the example of carbonate platforms in ***,based on the fact that the reasons for writing literature in key disciplines are likely to be provided by multidisciplinary experts,this paper designs a literature content extraction method that allows dealing with complex text ***,based on the literature extraction content,we formalize the entity extraction task(lexicon and lexical-based entity extraction)for entity ***,for testing the accuracy of entity extraction,three currently popular recognition methods are chosen to perform entity detection in this *** show that the entity data set provided by the lexicon and lexical-based entity extraction method is of significant assistance for the named entity recognition *** study presents a pilot study of entity extraction,which involves the use of a complex structure and specialized literature on carbonate platforms in English.

关键词： named entity recognition carbonate platform corpus entity extraction english literature detection