检索结果-南通市图书馆

Power entity recognition based on bidirectional long short-term memory and conditional random fields

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Global Energy Interconnection 2020年第2期3卷 186-192页

作者： Zhixiang Ji Xiaohui Wang Changyu Cai Hongjian Sun China Electric Power Research Institute Co.Ltd. Beijing100192P.R.China University of Durham The Palatine CentreStockton RoadDurhamDH13LEUK

With the application of artificial intelligence technology in the power industry,the knowledge graph is expected to play a key role in power grid dispatch processes,intelligent maintenance,and customer service response *** graphs are usually constructed based on entity ***,based on the mining of entity attributes and relationships,domain knowledge graphs can be constructed through knowledge *** this work,the entities and characteristics of power entity recognition are analyzed,the mechanism of entity recognition is clarified,and entity recognition techniques are analyzed in the context of the power *** entity recognition based on the conditional random fields (CRF) and bidirectional long short-term memory (BLSTM) models is investigated,and the two methods are comparatively *** results indicated that the CRF model,with an accuracy of 83%,can better identify the power entities compared to the *** CRF approach can thus be applied to the entity extraction for knowledge graph construction in the power field.

关键词： Knowledge graph entity recognition Conditional Random Fields(CRF) Bidirectional Long Short-Term Memory(BLSTM)

AI-based Automated Extraction of Location-Oriented COVID-19 Sentiments

维普期刊数据库

同方期刊数据库评论

在线全文

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2022年第8期72卷 3631-3649页

作者： Fahim K.Sufi Musleh Alsulami Federal Government MelbourneVIC3000Australia Information Systems Department Umm Al-Qura University(UQU)Makkah24382Saudi Arabia

The coronavirus disease(COVID-19)pandemic has affected the lives of social media users in an *** are constantly posting their satisfaction or dissatisfaction over the COVID-19 situation at their location of ***,understanding location-oriented sentiments about this situation is of prime importance for political leaders,and strategic *** this end,we present a new fully automated algorithm based on artificial intelligence(AI),for extraction of location-oriented public sentiments on the COVID-19 *** designed the proposed system to obtain exhaustive knowledge and insights on social media feeds related to COVID-19 in 110 languages through AI-based translation,sentiment analysis,location entity detection,and decomposition tree *** deployed fully automated algorithm on live Twitter feed from July 15,2021 and it is still running as of 12 January,*** system was evaluated on a limited dataset between July 15,2021 to August 10,*** this evaluation timeframe 150,000 tweets were analyzed and our algorithm found that 9,900 tweets contained one or more location *** total,13,220 location entities were detected during the evaluation period,and the rates of average precision and recall rate were 0.901 and 0.967,*** of 12 January,2022,the proposed solution has detected 43,169 locations using entity *** to the best of our knowledge,this study is the first to report location intelligence with entity detection,sentiment analysis,and decomposition tree analysis on social media messages related to COVID-19 and has covered the largest set of languages.

关键词： entity recognition AI-based social media monitoring sentiment analysis decision support system COVID-19

Weakly Supervised Abstractive Summarization with Enhancing Factual Consistency for Chinese Complaint Reports

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6期75卷 6201-6217页

作者： Ren Tao Chen Shuang Software College Northeastern UniversityShenyang110000China

A large variety of complaint reports reflect subjective information expressed by citizens.A key challenge of text summarization for complaint reports is to ensure the factual consistency of generated ***,in this paper,a simple and weakly supervised framework considering factual consistency is proposed to generate a summary of city-based complaint reports without pre-labeled sentences/***,it considers the importance of entity in complaint reports to ensure factual consistency of *** results on the customer review datasets(Yelp and Amazon)and complaint report dataset(complaint reports of Shenyang in China)show that the proposed framework outperforms state-of-the-art approaches in ROUGE scores and human *** unveils the effectiveness of our approach to helping in dealing with complaint reports.

关键词： Automatic summarization abstractive summarization weakly supervised training 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

面向企业模糊型技术需求的信息融合方法研究

A Knowledge-Enriched and Span-Based Network for Joint entity and Relation Extraction

运筹与模糊学 2024年第2期14卷 857页

作者：陶泽奎张志清武汉科技大学管理学院，湖北武汉

[目的/意义]：实现需求与供给的精准匹配是产教融合的关键所在，对科技成果转化、技术创新等具有重要意义。然而，企业在发布技术需求时，因商业机密保护等原因，导致技术需求文本描述常具有模糊性，难以与技术供给进行精准匹配。[方法... 详细信息

[目的/意义]：实现需求与供给的精准匹配是产教融合的关键所在，对科技成果转化、技术创新等具有重要意义。然而，企业在发布技术需求时，因商业机密保护等原因，导致技术需求文本描述常具有模糊性，难以与技术供给进行精准匹配。[方法/过程]：设计了面向企业模糊型技术需求的两阶段模型，首先使用BERT BiLSTM CRF算法提取企业发布项目中的需求实体，然后通过挖掘企业基本信息，结合需求实体作为检索条件爬取相关专利成果，通过TF-IDF算法获取专利中关键词作为补充信息，精准化技术需求描述，并识别出技术的聚焦方向与发展前沿。[结果/结论]：以机械制造领域内某企业真实需求作为案例，展示了该方法的完整处理过程，挖掘与TBM相关的施工管理、工件改进、系统研发等关键技术点；通过跨时间维度识别技术需求聚焦方向，TBM与人工智能、大数据、机器学习、探测等技术相结合将是未来发展的趋势。

关键词：技术需求多源化信息实体识别 BERT 关键词提取 Technical Requirement Multi-Source Information entity recognition BERT Keyword Extraction

来源：博看期刊评论

在线全文

博看期刊

学校读者我要写书评

暂无评论

Computers, Materials & Continua 2021年第7期68卷 377-389页

作者： Kun Ding Shanshan Liu Yuhao Zhang Hui Zhang Xiaoxiong Zhang Tongtong Wu Xiaolei Zhou The Sixty-Third Research Institute National University of Defense TechnologyNanjing210007China School of Computer Science and Technology Southeast UniversityNanjing211189China Faculty of Information Technology Monash UniversityMelbourne3800Australia

The joint extraction of entities and their relations from certain texts plays a significant role in most natural language *** entity and relation extraction in a specific domain,we propose a hybrid neural framework consisting of two parts:a span-based model and a graph-based *** span-based model can tackle overlapping problems compared with BILOU methods,whereas the graph-based model treats relation prediction as graph *** main contribution is to incorporate external lexical and syntactic knowledge of a specific domain,such as domain dictionaries and dependency structures from texts,into end-to-end neural *** conducted extensive experiments on a Chinese military entity and relation extraction *** results show that the proposed framework outperforms the baselines with better performance in terms of entity and relation *** proposed method provides insight into problems with the joint extraction of entities and their relations.

关键词： entity recognition relation extraction dependency parsing 1 Introduction