02554nam 2200637 a 450 991014150550332120170816131816.01-118-56265-81-299-18844-31-118-56287-91-118-56312-3(CKB)2670000000327559(EBL)1120432(OCoLC)826022184(SSID)ssj0000831569(PQKBManifestationID)11470846(PQKBTitleCode)TC0000831569(PQKBWorkID)10880697(PQKB)11685986(OCoLC)828198465(MiAaPQ)EBC1120432(EXLCZ)99267000000032755920120126d2012 uy 0engur|n|---|||||txtccrLead and nickel electrochemical batteries[electronic resource] /Christian Glaize, Sylvie GeniesHoboken, N.J. Wiley20121 online resource (315 p.)ISTEDescription based upon print version of record.1-84821-376-X Includes bibliographical references and index.pt. 1. Universal characteristics of batteries -- pt. 2. Lead-acid batteries -- pt. 3. Introduction to nickel-based batteries. The lead-acid accumulator was introduced in the middle of the 19th Century, the diverse variants of nickel accumulators between the beginning and the end of the 20th Century. Although old, these technologies are always very present on numerous markets. Unfortunately they are still not used in optimal conditions, often because of the misunderstanding of the internal electrochemical phenomena.This book will show that batteries are complex systems, made commercially available thanks to considerable amounts of scientific research, empiricism and practical knowledge. However, the design ofISTELead-acid batteriesNickel-cadmium batteriesNickel-metal hydride batteriesElectronic books.Lead-acid batteries.Nickel-cadmium batteries.Nickel-metal hydride batteries.621.31/242621.31242Glaize Christian958886Genies Sylvie958887MiAaPQMiAaPQMiAaPQBOOK9910141505503321Lead and nickel electrochemical batteries2201504UNINA10931nam 2200505 450 99649035460331620230302154838.03-031-17189-6(CKB)5840000000091732(MiAaPQ)EBC7101960(Au-PeEL)EBL7101960(PPN)26495338X(EXLCZ)99584000000009173220230302d2022 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierNatural language processing and Chinese computingPart II 11th CCF International Conference, NLPCC 2022, Guilin, China, September 24-25, 2022, proceedings /Wei Lu [and three others]Cham, Switzerland :Springer,[2022]©20221 online resource (385 pages)Lecture Notes in Computer Science3-031-17188-8 Intro -- Preface -- Organization -- Contents - Part II -- Contents - Part I -- Question Answering (Poster) -- Faster and Better Grammar-Based Text-to-SQL Parsing via Clause-Level Parallel Decoding and Alignment Loss -- 1 Introduction -- 2 Related Works -- 3 Our Proposed Model -- 3.1 Grammar-Based Text-to-SQL Parsing -- 3.2 Clause-Level Parallel Decoding -- 3.3 Clause-Level Alignment Loss -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Results -- 4.3 Analysis -- 5 Conclusions -- References -- Two-Stage Query Graph Selection for Knowledge Base Question Answering -- 1 Introduction -- 2 Our Approach -- 2.1 Query Graph Generation -- 2.2 Two-Stage Query Graph Selection -- 3 Experiments -- 3.1 Experimental Setup -- 3.2 Main Results -- 3.3 Discussion and Analysis -- 4 Related Work -- 5 Conclusions -- References -- Plug-and-Play Module for Commonsense Reasoning in Machine Reading Comprehension -- 1 Introduction -- 2 Methodology -- 2.1 Task Formulation -- 2.2 Proposed Module: PIECER -- 2.3 Plugging PIECER into MRC Models -- 3 Experiments -- 3.1 Datasets -- 3.2 Base Models -- 3.3 Experimental Settings -- 3.4 Main Results -- 3.5 Analysis and Discussions -- 4 Related Work -- 5 Conclusion -- References -- Social Media and Sentiment Analysis (Poster) -- FuDFEND: Fuzzy-Domain for Multi-domain Fake News Detection -- 1 Introduction -- 2 Related Work -- 2.1 Fake News Detection Methods -- 2.2 Multi-domain Rumor Task -- 3 FuDFEND: Fuzzy-Domain Fake News Detection Model -- 3.1 Membership Function -- 3.2 Feature Extraction -- 3.3 Domain Gate -- 3.4 Fake News Prediction and Loss Function -- 4 Experiment -- 4.1 Dataset -- 4.2 Experiment Setting -- 4.3 Train Membership Function and FuDFEND -- 4.4 Experiment on Weibo21 -- 4.5 Experiment on Thu Dataset -- 5 Conclusion -- 6 Future Work -- References -- NLP Applications and Text Mining (Poster).Continuous Prompt Enhanced Biomedical Entity Normalization -- 1 Introduction -- 2 Related Work -- 2.1 Biomedical Entity Normalization -- 2.2 Prompt Learning and Contrastive Loss -- 3 Our Method -- 3.1 Prompt Enhanced Scoring Mechanism -- 3.2 Contrastive Loss Enhanced Training Mechanism -- 4 Experiments and Analysis -- 4.1 Dataset and Evaluation -- 4.2 Data Preprocessing -- 4.3 Experiment Setting -- 4.4 Overall Performance -- 4.5 Ablation Study -- 5 Conclusion -- References -- Bidirectional Multi-channel Semantic Interaction Model of Labels and Texts for Text Classification -- 1 Introduction -- 2 Model -- 2.1 Preliminaries -- 2.2 Bidirectional Multi-channel Semantic Interaction Model -- 3 Experiments -- 3.1 Experimental Settings -- 3.2 Results and Analysis -- 3.3 Ablation Test -- 4 Conclusions -- References -- Exploiting Dynamic and Fine-grained Semantic Scope for Extreme Multi-label Text Classification -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Notation -- 3.2 TReaderXML -- 4 Experiments -- 4.1 Datasets and Preprocessing -- 4.2 Baselines -- 4.3 Evaluation Metrics -- 4.4 Ablation Study -- 4.5 Performance on Tail Labels -- 5 Conclusions -- References -- MGEDR: A Molecular Graph Encoder for Drug Recommendation -- 1 Introduction -- 2 Related Works -- 2.1 Drug Recommendation -- 2.2 Molecular Graph Representation -- 3 Problem Formulation -- 4 The MGEDR Model -- 4.1 Patient Encoder -- 4.2 Medicine Encoder -- 4.3 Functional Groups Encoder -- 4.4 Medicine Representation -- 4.5 Optimization -- 5 Experiments -- 5.1 Dataset and Metrics -- 5.2 Results -- 5.3 Ablations -- 6 Conclusion -- References -- Student Workshop (Poster) -- Semi-supervised Protein-Protein Interactions Extraction Method Based on Label Propagation and Sentence Embedding -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Problem Formulation -- 3.2 Overall Workflow.3.3 Label Propagation -- 3.4 Sentence Embedding -- 3.5 CNN Classifier -- 4 Results -- 4.1 Datasets and Preprocessing -- 4.2 Experimental Results -- 4.3 Hyperparameter Analysis -- 5 Conclusion -- References -- Construction and Application of a Large-Scale Chinese Abstractness Lexicon Based on Word Similarity -- 1 Introduction -- 2 Data and Method -- 2.1 Data -- 2.2 Method -- 3 Experiment -- 4 Construction and Evaluation -- 5 Application -- 5.1 Cross-Language Comparison -- 5.2 Chinese Text Readability Auto-evaluation -- 6 Conclusion -- References -- Stepwise Masking: A Masking Strategy Based on Stepwise Regression for Pre-training -- 1 Introduction -- 2 Methodology -- 2.1 Three-Stage Framework -- 2.2 Stepwise Masking -- 3 Experiments -- 3.1 Datasets -- 3.2 Experimental Settings -- 3.3 Main Results -- 3.4 Effectiveness of Stepwise Masking -- 3.5 Effect of Dynamic in Stepwise Masking -- 3.6 Case Study -- 4 Conclusion and Future Work -- References -- Evaluation Workshop (Poster) -- Context Enhanced and Data Augmented W2NER System for Named Entity Recognition -- 1 Introduction -- 2 Related Work -- 3 The Proposed Approach -- 3.1 Task Definition -- 3.2 Model Structure -- 3.3 Data Augmentation -- 3.4 Result Ensemble -- 4 Experiments -- 4.1 Dataset and Metric -- 4.2 Experiment Settings -- 4.3 Baselines -- 4.4 Results and Analysis -- 5 Conclusion -- References -- Multi-task Hierarchical Cross-Attention Network for Multi-label Text Classification -- 1 Introduction -- 2 Related Work -- 2.1 Hierarchical Multi-label Text Classification -- 2.2 Representation of Scientific Literature -- 3 Methodology -- 3.1 Representation Layer -- 3.2 Hierarchical Cross-Attention Recursive Layer -- 3.3 Hierarchical Prediction Layer -- 3.4 Rebalanced Loss Function -- 4 Experiment -- 4.1 Dataset and Evaluation -- 4.2 Experimental Settings -- 4.3 Results and Discussions.4.4 Module Analysis -- 5 Conclusion -- References -- An Interactive Fusion Model for Hierarchical Multi-label Text Classification -- 1 Introduction -- 2 Related Work -- 3 Task Definition -- 4 Method -- 4.1 Shared Encoder Module -- 4.2 Task-Specific Module -- 4.3 Training and Inference -- 5 Experiment -- 6 Conclusion -- References -- Scene-Aware Prompt for Multi-modal Dialogue Understanding and Generation -- 1 Introduction -- 2 Task Introduction -- 2.1 Problem Definition -- 2.2 Evaluation Metric -- 2.3 Dateset -- 3 Main Methods -- 3.1 Multi-tasking Multi-modal Dialogue Understanding -- 3.2 Scene-Aware Prompt Multi-modal Dialogue Generation -- 3.3 Training and Inference -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Main Results -- 4.3 Ablation Study -- 4.4 Online Results -- 5 Conclusion -- References -- BIT-WOW at NLPCC-2022 Task5 Track1: Hierarchical Multi-label Classification via Label-Aware Graph Convolutional Network -- 1 Introduction -- 2 Approach -- 2.1 Context-Aware Label Embedding -- 2.2 Graph-Based Hierarchical Label Modeling -- 2.3 Curriculum Learning Strategy -- 2.4 Ensemble Learning and Post Editing -- 3 Experiments -- 3.1 Dataset and Experiment Settings -- 3.2 Main Results -- 3.3 Analysis -- 4 Related Work -- 5 Conclusion -- References -- CDAIL-BIAS MEASURER: A Model Ensemble Approach for Dialogue Social Bias Measurement -- 1 Introduction -- 2 Related Work -- 2.1 Shared Tasks -- 2.2 Solution Models -- 3 Dataset -- 4 Method -- 4.1 Models Selection -- 4.2 Fine-Tuning Strategies -- 4.3 Ensembling Strategy -- 5 Result -- 5.1 Preliminary Screening -- 5.2 Model Ensemble -- 5.3 Ensemble Size Effect -- 5.4 Discussion -- 6 Conclusion -- References -- A Pre-trained Language Model for Medical Question Answering Based on Domain Adaption -- 1 Introduction -- 2 Related Work -- 2.1 Encoder-Based -- 2.2 Decoder-Based -- 2.3 Encoder-Decoder-Based.3 Description of the Competition -- 3.1 Evaluation Metrics -- 3.2 Datasets -- 4 Solution -- 4.1 Model Introduction -- 4.2 Strategy -- 4.3 Model Optimization -- 4.4 Model Evaluation -- 5 Conclusion -- References -- Enhancing Entity Linking with Contextualized Entity Embeddings -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Dual Encoder -- 3.2 LUKE-Based Cross-Encoder -- 4 Experiments -- 4.1 Data -- 4.2 Candidate Retrieval -- 4.3 Candidate Reranking -- 5 Conclusion -- References -- A Fine-Grained Social Bias Measurement Framework for Open-Domain Dialogue Systems -- 1 Introduction -- 2 Related Work -- 2.1 Fine Grained Dialogue Social Bias Measurement -- 2.2 Application of Contrastive Learning in NLP Tasks -- 2.3 Application of Prompt Learning in NLP Tasks -- 3 Fine-Grain Dialogue Social Bias Measurement Framework -- 3.1 General Representation Module -- 3.2 Two-Stage Prompt Learning Module -- 3.3 Contrastive Learning Module -- 4 Experiment -- 4.1 Dataset -- 4.2 Experimental Setup -- 4.3 Results and Analysis -- 5 Conclusion -- References -- Dialogue Topic Extraction as Sentence Sequence Labeling -- 1 Introduction -- 2 Related Work -- 2.1 Dialogue Topic Information -- 2.2 Sequence Labeling -- 3 Methodology -- 3.1 Task Definition -- 3.2 Topic Extraction Model -- 3.3 Ensemble Model -- 4 Experiments -- 4.1 Dataset -- 4.2 Results and Analysis -- 5 Conclusion -- References -- Knowledge Enhanced Pre-trained Language Model for Product Summarization -- 1 Introduction -- 2 Related Work -- 2.1 Encoder-Decoder Transformer -- 2.2 Decoder-Only Transformer -- 3 Description of the Competition -- 4 Dataset Introduction -- 4.1 Textual Data -- 4.2 Image Data -- 5 Model Solution -- 5.1 Model Introduction -- 5.2 Model Training -- 6 Model Evaluation -- 7 Conclusion -- References -- Augmented Topic-Specific Summarization for Domain Dialogue Text -- 1 Introduction.2 Related Work.Lecture notes in computer science.Chinese languageData processingNatural language processing (Computer science)Chinese languageData processing.Natural language processing (Computer science)495.10285Lu WeiMiAaPQMiAaPQMiAaPQBOOK996490354603316Natural Language Processing and Chinese Computing1912515UNISA