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

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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 provision.Knowledge graphs are usually constructed based on entity recognition.Specifically,based on the mining of entity attributes and relationships,domain knowledge graphs can be constructed through knowledge fusion.In 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 domain.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 analyzed.The results indicated that the CRF model,with an accuracy of 83%,can better identify the power entities compared to the BLSTM.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

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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 unprecedentedmanner.They are constantly posting their satisfaction or dissatisfaction over the COVID-19 situation at their location of interest.Therefore,understanding location-oriented sentiments about this situation is of prime importance for political leaders,and strategic decision-makers.To 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 situation.We 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 analysis.We deployed fully automated algorithm on live Twitter feed from July 15,2021 and it is still running as of 12 January,2022.The system was evaluated on a limited dataset between July 15,2021 to August 10,2021.During this evaluation timeframe 150,000 tweets were analyzed and our algorithm found that 9,900 tweets contained one or more location entities.In 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,respectively.As of 12 January,2022,the proposed solution has detected 43,169 locations using entity recognition.According 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

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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 summary.Therefore,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/words.Furthermore,it considers the importance of entity in complaint reports to ensure factual consistency of summary.Experimental 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 evaluation.It 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

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

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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 processes.For 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 model.The span-based model can tackle overlapping problems compared with BILOU methods,whereas the graph-based model treats relation prediction as graph classification.Our 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 models.We conducted extensive experiments on a Chinese military entity and relation extraction corpus.The results show that the proposed framework outperforms the baselines with better performance in terms of entity and relation prediction.The 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