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[null,"1.","Record Nr.",null,"UNISA996391560203316"]
[null,null,"Autore",null,"Waring Robert <1614-1658.>"]
[null,null,"Titolo",null,"A sermon preached at St. Margarets in Westminster at the funeral of Mrs. Susanna Gray, daughter of Henry Gray, Esq. of Enfield in Staffordshire, who on the 29 of October, 1654, began her eternal sabbath [[electronic resource] /] / by Robert Waring"]
[null,null,"Pubbl/distr/stampa",null,"London, : [s.n.], Printed in the year 1676"]
[null,null,"Descrizione fisica",null,"95 p"]
[null,null,"Soggetti",null,"Funeral sermons"]
[{"$nulls":4},"Sermons, English - 17th century"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Note generali","Reproduction of original in the Cambridge University Library."]
[null,null,null,"Sommario/riassunto","eebo-0021"]
[null,"2.","Record Nr.",null,"UNISA996490354703316"]
[null,null,"Titolo",null,"Natural language processing and Chinese computing : 11th CCF international conference, NLPCC 2022, Guilin, China, September 24-25, 2022, proceedings, Part I / / edited by Wei Lu [and three others]"]
[null,null,"Pubbl/distr/stampa",null,"Cham, Switzerland : , : Springer, , [2022]"]
[{"$nulls":4},"©2022"]
[null,null,"ISBN",null,"3-031-17120-9"]
[null,null,"Descrizione fisica",null,"1 online resource (878 pages)"]
[null,null,"Collana",null,"Lecture Notes in Computer Science ; ; v.13551"]
[null,null,"Disciplina",null,"495.10285"]
[null,null,"Soggetti",null,"Chinese language - Data processing"]
[{"$nulls":4},"Natural language processing (Computer science)"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"Intro -- Preface -- Organization -- Contents - Part I -- Contents - Part II -- Fundamentals of NLP (Oral) -- Exploiting Word Semantics to Enrich Character Representations of Chinese Pre-trained Models -- 1 Introduction -- 2 Related Work -- 3 Multiple Word Segmentation Aggregation -- 4 Projecting Word Semantics to Character Representation -- 4.1 Integrating Word Embedding to Character Representation -- 4.2 Mixing Character Representations Within a Word -- 4.3 Fusing New Character Embedding to Sentence Representation -- 5 Experimental Setup -- 5.1 Tasks and Datasets -- 5.2 Baseline Models -- 5.3 Training Details -- 6 Results and Analysis -- 6.1 Overall Results -- 6.2 Ablation Study -- 6.3 Case Study -- 7 Conclusion -- References -- PGBERT: Phonology and Glyph Enhanced Pre-training for Chinese Spelling Correction -- 1 Introduction -- 2 Related Work -- 3 Our Approach -- 3.1 Problem and Motivation -- 3.2 Model -- 4 Experiment -- 4.1 Pre-training -- 4.2 Fine Tuning -- 4.3 Parameter Setting -- 4.4 Baseline Models -- 4.5 Main Results -- 4.6 Ablation Experiments -- 5 Conclusions -- References -- MCER: A Multi-domain Dataset for Sentence-Level Chinese Ellipsis Resolution -- 1 Introduction -- 2 Definition of Ellipsis -- 2.1 Ellipsis for Chinese NLP -- 2.2 Explanations -- 3 Dataset -- 3.1 Annotation -- 3.2 Dataset Analysis -- 3.3 Annotation Format -- 3.4 Considerations -- 4 Experiments -- 4.1 Baseline Methods -- 4.2 Evaluation Metrics -- 4.3 Results -- 5 Conclusion -- References -- Two-Layer Context-Enhanced "]
[{"$nulls":4},"Representation for Better Chinese Discourse Parsing -- 1 Introduction -- 2 Related Work -- 3 Model -- 3.1 Basic Principles of Transition-Based Approach -- 3.2 Bottom Layer of Enhanced Context Representation: Intra-EDU Encoder with GCN -- 3.3 Upper Layer of Enhanced Context Representation: Inter-EDU Encoder with Star-Transformer -- 3.4 SPINN-Based Decoder."]
[{"$nulls":4},"3.5 Training Loss -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Overall Experimental Results -- 4.3 Compared with Other Parsing Framework -- 5 Conclusion -- References -- How Effective and Robust is Sentence-Level Data Augmentation for Named Entity Recognition? -- 1 Introduction -- 2 Methodology -- 2.1 CMix -- 2.2 CombiMix -- 2.3 TextMosaic -- 3 Experiment -- 3.1 Datasets -- 3.2 Experimental Setup -- 3.3 Results of Effectiveness Evaluation -- 3.4 Study of the Sample Size After Data Augmentation -- 3.5 Results of Robustness Evaluation -- 3.6 Results of CCIR Cup -- 4 Conclusion -- References -- Machine Translation and Multilinguality (Oral) -- Random Concatenation: A Simple Data Augmentation Method for Neural Machine Translation -- 1 Introduction -- 2 Related Works -- 3 Approach -- 3.1 Vanilla Randcat -- 3.2 Randcat with Back-Translation -- 4 Experiment -- 4.1 Experimental Setup -- 4.2 Translation Performance -- 4.3 Analysis -- 4.4 Additional Experiments -- 5 Conclusions -- References -- Contrastive Learning for Robust Neural Machine Translation with ASR Errors -- 1 Introduction -- 2 Related Work -- 2.1 Robust Neural Machine Translation -- 2.2 Contrastive Learning -- 3 NISTasr Test Dataset -- 4 Our Approach -- 4.1 Overview -- 4.2 Constructing Perturbed Inputs -- 5 Experimentation -- 5.1 Experimental Settings -- 5.2 Experimental Results -- 5.3 Ablation Analysis -- 5.4 Effect on Hyper-Parameter -- 5.5 Case Study -- 6 Conclusion -- References -- An Enhanced New Word Identification Approach Using Bilingual Alignment -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Architecture -- 3.2 Multi-new Model -- 3.3 Bilingual Identification Algorithm -- 4 Experiment -- 4.1 Datasets -- 4.2 Results of Multi-new Model -- 4.3 Results of NEWBA-P Model and NEWBA-E Model -- 5 Conclusions -- References -- Machine Learning for NLP (Oral)."]
[{"$nulls":4},"Multi-task Learning with Auxiliary Cross-attention Transformer for Low-Resource Multi-dialect Speech Recognition -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Two Task Streams -- 3.2 Auxiliary Cross-attention -- 4 Experiment -- 4.1 Data -- 4.2 Settings -- 4.3 Experimental Results -- 5 Conclusions -- References -- Regularized Contrastive Learning of Semantic Search -- 1 Introduction -- 2 Related Work -- 3 Regularized Contrastive Learning -- 3.1 Task Description -- 3.2 Data Augmentation -- 3.3 Contrastive Regulator -- 3.4 Anisotropy Problem -- 4 Experiments -- 4.1 Datasets -- 4.2 Training Details -- 4.3 Results -- 4.4 Ablation Study -- 5 Conclusion -- A  APPENDIX -- A.1 A Training Details -- References -- Kformer: Knowledge Injection in Transformer Feed-Forward Layers -- 1 Introduction -- 2 Knowledge Neurons in the FFN -- 3 Kformer: Knowledge Injection in FFN -- 3.1 Knowledge Retrieval -- 3.2 Knowledge Embedding -- 3.3 Knowledge Injection -- 4 Experiments -- 4.1 Dataset -- 4.2 Experiment Setting -- 4.3 Experiments Results -- 5 Analysis -- 5.1 Impact of Top N Knowledge -- 5.2 Impact of Layers -- 5.3 Interpretability -- 6 Related Work -- 7 Conclusion and Future Work -- References -- Doge Tickets: Uncovering Domain-General Language Models by Playing Lottery Tickets -- 1 Introduction -- 2 Background -- 2.1 Out-of-domain Generalization -- 2.2 Lottery Ticket Hypothesis -- 2.3 Transformer Architecture -- 3 Identifying Doge Tickets -- 3.1 Uncovering Domain-general LM -- 3.2 Playing Lottery Tickets -- 4 Experiments -- 4.1 Datasets -- 4.2 Models and Implementation -- 4.3 "]
[{"$nulls":4},"Main Comparison -- 5 Analysis -- 5.1 Sensitivity to Learning Variance -- 5.2 Impact of the Number of Training Domains -- 5.3 Existence of Domain-specific Manner -- 5.4 Consistency with Varying Sparsity Levels -- 6 Conclusions -- References."]
[{"$nulls":4},"Information Extraction and Knowledge Graph (Oral) -- BART-Reader: Predicting Relations Between Entities via Reading Their Document-Level Context Information -- 1 Introduction -- 2 Task Formulation -- 3 BART-Reader -- 3.1 Entity-aware Document Context Representation -- 3.2 Entity-Pair Representation -- 3.3 Relation Prediction -- 3.4 Loss Function -- 4 Experiments -- 4.1 Dataset -- 4.2 Experiment Settings -- 4.3 Main Results -- 4.4 Ablation Study -- 4.5 Cross-attention Attends on Proper Mentions -- 5 Related Work -- 6 Conclusion -- References -- DuEE-Fin: A Large-Scale Dataset for Document-Level Event Extraction -- 1 Introduction -- 2 Preliminary -- 2.1 Concepts -- 2.2 Task Definition -- 2.3 Challenges of DEE -- 3 Dataset Construction -- 3.1 Event Schema Construction -- 3.2 Candidate Data Collection -- 3.3 Annotation Process -- 4 Data Analysis -- 4.1 Overall Statics -- 4.2 Event Types and Argument Roles -- 4.3 Comparison with Existing Benchmarks -- 5 Experiment -- 5.1 Baseline -- 5.2 Evaluation Metric -- 5.3 Results -- 6 Conclusion -- References -- Temporal Relation Extraction on Time Anchoring and Negative Denoising -- 1 Introduction -- 2 Related Work -- 3 TAM: Time Anchoring Model for TRE -- 3.1 Mention Embedding Module -- 3.2 Multi-task Learning Module -- 3.3 Interval Anchoring Module -- 3.4 Negative Denoising Module -- 4 Experimentation -- 4.1 Datasets and Experimental Settings -- 4.2 Results -- 4.3 Ablation Study -- 4.4 Effects of Learning Curves -- 4.5 Case Study and Error Analysis -- 5 Conclusion -- References -- Label Semantic Extension for Chinese Event Extraction -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Event Type Detection -- 3.2 Label Semantic Extension -- 3.3 Event Extraction -- 4 Experiments -- 4.1 Dataset and Experiment Setup -- 4.2 Main Result -- 4.3 Ablation Study -- 4.4 Effect of Threshold -- 5 Conclusions."]
[{"$nulls":4},"References -- QuatSE: Spherical Linear Interpolation of Quaternion for Knowledge Graph Embeddings -- 1 Introduction -- 2 Related Work -- 3 Proposed Model -- 3.1 Quaternion Background -- 3.2 QuatSE -- 3.3 Theoretical Analysis -- 4 Experiment -- 4.1 Datasets -- 4.2 Evaluation Protocol -- 4.3 Implementation Details -- 4.4 Baselines -- 5 Results and Analysis -- 5.1 Main Results -- 5.2 1-N, N-1 and Multiple-Relations Pattern -- 6 Conclusion -- References -- Entity Difference Modeling Based Entity Linking for Question Answering over Knowledge Graphs -- 1 Introduction -- 2 Related Work -- 2.1 Entity Representation -- 2.2 Model Architecture -- 3 Framework -- 3.1 Question Encoder -- 3.2 Entity Encoder -- 3.3 Mention Detection and Entity Disambiguation -- 4 Experiments -- 4.1 Model Comparison -- 4.2 Ablation Study -- 4.3 Case Study -- 5 Conclusion -- References -- BG-EFRL: Chinese Named Entity Recognition Method and Application Based on Enhanced Feature Representation -- 1 Introduction -- 2 Related Work -- 2.1 Chinese Named Entity Recognition -- 2.2 Embedding Representation -- 3 NER Model -- 3.1 Embedding Representation -- 3.2 Initialize the Graph Structure -- 3.3 Encoders -- 3.4 Feature Enhancer -- 3.5 Decoder -- 4 Experiments -- 4.1 Datasets and Metrics -- 4.2 Implementation Details -- 4.3 Comparison Methods -- 4.4 Results -- 5 Conclusion -- References -- TEMPLATE: TempRel Classification Model Trained with Embedded Temporal Relation Knowledge -- 1 Introduction -- 2 Related Work -- 3 Our Baseline Model -- 4 TEMPLATE Approach -- 4.1 Build Templates -- 4.2 Embedded Knowledge of TempRel Information -- 4.3 Train the Model with Embedded Knowledge of TempRel Information -- 5 Experiments "]
[{"$nulls":4},"and Results -- 5.1 Data-set -- 5.2 Experimental Setup -- 5.3 Main Results -- 5.4 Ablation Study and Qualitative Analysis -- 6 Conclusion -- References."]
[{"$nulls":4},"Dual Interactive Attention Network for Joint Entity and Relation Extraction."]
[null,"3.","Record Nr.",null,"UNINA9910154509403321"]
[null,null,"Titolo",null,"Mitochondrial DNA"]
[null,null,"Pubbl/distr/stampa",null,"[London, Eng.] : , : Informa Healthcare, , [2008-2015]"]
[{"$nulls":4},"©2008-©2015"]
[null,null,"ISSN",null,"1940-1744"]
[null,null,"Disciplina",null,"574.87/3282"]
[null,null,"Soggetti",null,"Nucleotide sequence"]
[{"$nulls":4},"Mitochondrial DNA"]
[{"$nulls":4},"Genètica molecular - Revistes"]
[{"$nulls":4},"ADN"]
[{"$nulls":4},"ADN mitocondrial"]
[{"$nulls":4},"Genètica molecular"]
[{"$nulls":4},"Mitocondris"]
[{"$nulls":4},"Revistes electròniques."]
[{"$nulls":4},"Periodicals."]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Periodico"]
[null,null,null,"Note generali","Refereed/Peer-reviewed"]