Natural Language Processing and Chinese Computing : 11th CCF International Conference, NLPCC 2022, Guilin, China, September 24–25, 2022, Proceedings, Part II / / edited by Wei Lu, Shujian Huang, Yu Hong, Xiabing Zhou

• Pubblicazione: Cham : , : Springer Nature Switzerland : , : Imprint : Springer, , 2022
• Edizione: 1st ed. 2022.
• Descrizione fisica: 1 online resource (385 pages)
• Disciplina: 495.10285; 006.35
• Soggetto topico: Artificial intelligence; Artificial Intelligence
• Persona (resp. second.): LuWei
• Nota di contenuto: 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.
• Sommario/riassunto: This two-volume set of LNAI 13551 and 13552 constitutes the refereed proceedings of the 11th CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2022, held in Guilin, China, in September 2022. The 62 full papers, 21 poster papers, and 27 workshop papers presented were carefully reviewed and selected from 327 submissions. They are organized in the following areas: Fundamentals of NLP; Machine Translation and Multilinguality; Machine Learning for NLP; Information Extraction and Knowledge Graph; Summarization and Generation; Question Answering; Dialogue Systems; Social Media and Sentiment Analysis; NLP Applications and Text Mining; and Multimodality and Explainability.
• Titolo autorizzato: Natural Language Processing and Chinese Computing
• ISBN: 9783031171895; 3031171896
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910595031503321
• Lo trovi qui: Univ. Federico II
• Serie: Lecture Notes in Artificial Intelligence, . 2945-9141 ; ; 13552