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Chinese computational linguistics : 22nd China National Conference, CCL 2023, Harbin, China, August 3-5, 2023 : proceedings / / Maosong Sun, Bing Qin, Xipeng Qiu, Jiang Jing, Xianpei Han, Gaoqi Rao, Yubo Chen, editors
| Chinese computational linguistics : 22nd China National Conference, CCL 2023, Harbin, China, August 3-5, 2023 : proceedings / / Maosong Sun, Bing Qin, Xipeng Qiu, Jiang Jing, Xianpei Han, Gaoqi Rao, Yubo Chen, editors |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Singapore : , : Springer, , 2023 |
| Descrizione fisica | 1 online resource (xxii, 466 pages) : illustrations (chiefly color) |
| Altri autori (Persone) |
SunMaosong
QinBing (Professor of computer science) QiuXipeng JingJiang HanXianpei RaoGaoqi ChenYubo |
| Collana | Lecture Notes in Computer Science Series |
| Soggetto topico |
Big data
Chinese language - Data processing Computational linguistics Natural language processing (Computer science) |
| ISBN | 981-9962-07-2 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996550554903316 |
| Singapore : , : Springer, , 2023 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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Chinese computational linguistics : 20th China national conference, CCL 2021, Hohhot, China, August 13-15, 2021 : proceedings / / Sheng Li [and seven others] (editors)
| Chinese computational linguistics : 20th China national conference, CCL 2021, Hohhot, China, August 13-15, 2021 : proceedings / / Sheng Li [and seven others] (editors) |
| Edizione | [1st ed. 2021.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2021] |
| Descrizione fisica | 1 online resource (XVI, 486 p. 230 illus., 103 illus. in color.) |
| Disciplina | 495.10183 |
| Collana | Lecture notes in computer science. Lecture notes in artificial intelligence |
| Soggetto topico | Chinese language - Data processing |
| ISBN | 3-030-84186-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Machine Translation and Multilingual Information Processing -- Minority Language Information Processing -- Social Computing and Sentiment Analysis -- Text Generation and Summarization -- Information Retrieval, Dialogue and Question Answering -- Linguistics and Cognitive Science -- Language Resource and Evaluation -- Knowledge Graph and Information Extraction -- NLP Applications. |
| Record Nr. | UNISA-996464445003316 |
| Cham, Switzerland : , : Springer, , [2021] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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HowNet and the computation of meaning [[electronic resource] /] / Zhendong Dong, Qiang Dong
| HowNet and the computation of meaning [[electronic resource] /] / Zhendong Dong, Qiang Dong |
| Autore | Dong Zhendong |
| Pubbl/distr/stampa | Hackensack, NJ, : World Scientific, c2006 |
| Descrizione fisica | 1 online resource (318 p.) |
| Disciplina | 401.43 |
| Altri autori (Persone) | DongQiang |
| Soggetto topico |
Semantics - Data processing
Lexicology - Data processing Phraseology - Data processing Natural language processing (Computer science) Machine translating Chinese language - Data processing English language - Data processing |
| Soggetto genere / forma | Electronic books. |
| ISBN |
1-281-91962-4
9786611919627 981-277-467-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. Meaning and its representation. 1.1. Concept Relation Net (CRN). 1.2. Attribute Relation Net (ARN) -- 2. Overview of HowNet. 2.1. The history of HowNet. 2.2. The nature of HowNet. 2.3. The architecture of HowNet -- 3. Sememes. 3.1. What is a Sememe. 3.2. Acquisition of a Sememe set. 3.3. Inspiration from Chinese -- 4. Concept classification and property description. 4.1. Concept classification. 4.2. Arbitrariness and purpose of classification. 4.3. Classification by top-down or bottom-up. 4.4. General guidelines for concept classification in HowNet. 4.5. Root-nodes in HowNet -- 5. Semantic roles. 5.1. What is a semantic role. 5.2. Semantic roles in HowNet -- 6. Taxonomy. 6.1. Event taxonomy. 6.2. Entity taxonomy. 6.3. Attribute taxonomy. 6.4. Attribute-value taxonomy. 6.5. Secondary features list. 6.6. Antonymous and converse Sememes lists -- 7. Knowledge dictionary of HowNet. 7.1. Lexical entry. 7.2. Part-of-Speech (POS). 7.3. Example. 7.4. Concept definition -- 8. Knowledge database mark-up language and concept defining. 8.1. Extended BNF of KDML. 8.2. Identifiers of KDML and referent signs. 8.3. Defining regulations. 8.4. Principles of defining concepts -- 9. Revelation of relations in HowNet. 9.1. Explicit relations. 9.2. Implicit relations. 9.3. Axiomatic relation of events and role shifting -- 10. Browser - HowNet's device of computation of meaning. 10.1. Dictionary page. 10.2. Taxonomy page -- 11. Secondary resources - HowNet's devices of computation of meaning. 11.1. Concept Relevance Calculator (CRC). 11.2. Concept Similarity Measure (CSM). 11.3. Query Expansion Tool (QET) -- 12. HowNet as a software. 12.1. Data construction. 12.2. Application Program Interface (API) of HowNet -- 13. New resources activating new technology - some applications of HowNet. 13.1. Word Sense Disambiguation (WSD). 13.2. Question analysis in question answering. 13.3. Domain-specific seed word list updating -- 14. Some views of Chinese through HowNet. 14.1. Words or no words. 14.2. Part-of-speech - semantics-first. 14.3. Aspect of Chinese verbs. |
| Record Nr. | UNINA-9910451402203321 |
Dong Zhendong
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| Hackensack, NJ, : World Scientific, c2006 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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HowNet and the computation of meaning [[electronic resource] /] / Zhendong Dong, Qiang Dong
| HowNet and the computation of meaning [[electronic resource] /] / Zhendong Dong, Qiang Dong |
| Autore | Dong Zhendong |
| Pubbl/distr/stampa | Hackensack, NJ, : World Scientific, c2006 |
| Descrizione fisica | 1 online resource (318 p.) |
| Disciplina | 401.43 |
| Altri autori (Persone) | DongQiang |
| Soggetto topico |
Semantics - Data processing
Lexicology - Data processing Phraseology - Data processing Natural language processing (Computer science) Machine translating Chinese language - Data processing English language - Data processing |
| ISBN |
1-281-91962-4
9786611919627 981-277-467-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | 1. Meaning and its representation. 1.1. Concept Relation Net (CRN). 1.2. Attribute Relation Net (ARN) -- 2. Overview of HowNet. 2.1. The history of HowNet. 2.2. The nature of HowNet. 2.3. The architecture of HowNet -- 3. Sememes. 3.1. What is a Sememe. 3.2. Acquisition of a Sememe set. 3.3. Inspiration from Chinese -- 4. Concept classification and property description. 4.1. Concept classification. 4.2. Arbitrariness and purpose of classification. 4.3. Classification by top-down or bottom-up. 4.4. General guidelines for concept classification in HowNet. 4.5. Root-nodes in HowNet -- 5. Semantic roles. 5.1. What is a semantic role. 5.2. Semantic roles in HowNet -- 6. Taxonomy. 6.1. Event taxonomy. 6.2. Entity taxonomy. 6.3. Attribute taxonomy. 6.4. Attribute-value taxonomy. 6.5. Secondary features list. 6.6. Antonymous and converse Sememes lists -- 7. Knowledge dictionary of HowNet. 7.1. Lexical entry. 7.2. Part-of-Speech (POS). 7.3. Example. 7.4. Concept definition -- 8. Knowledge database mark-up language and concept defining. 8.1. Extended BNF of KDML. 8.2. Identifiers of KDML and referent signs. 8.3. Defining regulations. 8.4. Principles of defining concepts -- 9. Revelation of relations in HowNet. 9.1. Explicit relations. 9.2. Implicit relations. 9.3. Axiomatic relation of events and role shifting -- 10. Browser - HowNet's device of computation of meaning. 10.1. Dictionary page. 10.2. Taxonomy page -- 11. Secondary resources - HowNet's devices of computation of meaning. 11.1. Concept Relevance Calculator (CRC). 11.2. Concept Similarity Measure (CSM). 11.3. Query Expansion Tool (QET) -- 12. HowNet as a software. 12.1. Data construction. 12.2. Application Program Interface (API) of HowNet -- 13. New resources activating new technology - some applications of HowNet. 13.1. Word Sense Disambiguation (WSD). 13.2. Question analysis in question answering. 13.3. Domain-specific seed word list updating -- 14. Some views of Chinese through HowNet. 14.1. Words or no words. 14.2. Part-of-speech - semantics-first. 14.3. Aspect of Chinese verbs. |
| Record Nr. | UNINA-9910777061003321 |
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Dong Zhendong
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| Hackensack, NJ, : World Scientific, c2006 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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International journal of computer processing of oriental languages
| International journal of computer processing of oriental languages |
| Pubbl/distr/stampa | Singapore ; ; Hong Kong, : World Scientific, ©2000- |
| Descrizione fisica | 1 online resource |
| Disciplina |
495.1/0285
495.0285 |
| Soggetto topico |
Chinese language - Data processing
Oriental languages - Data processing Chinese characters - Data processing Information storage and retrieval systems - Chinese language Information storage and retrieval systems - Chinese characters Chinois (Langue) - Informatique Langues orientales - Informatique Chinois (Langue) - Caractères - Informatique |
| Soggetto genere / forma | Periodicals. |
| ISSN | 1793-6748 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | Computer processing of oriental languages |
| Record Nr. | UNINA-9910338732103321 |
| Singapore ; ; Hong Kong, : World Scientific, ©2000- | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
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Natural language processing and Chinese computing . Part II : 11th CCF International Conference, NLPCC 2022, Guilin, China, September 24-25, 2022, proceedings / / Wei Lu [and three others]
| Natural language processing and Chinese computing . Part II : 11th CCF International Conference, NLPCC 2022, Guilin, China, September 24-25, 2022, proceedings / / Wei Lu [and three others] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (385 pages) |
| Disciplina | 495.10285 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Chinese language - Data processing
Natural language processing (Computer science) |
| ISBN | 3-031-17189-6 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| 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. |
| Record Nr. | UNISA-996490354603316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
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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]
| 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] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (878 pages) |
| Disciplina | 495.10285 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico |
Chinese language - Data processing
Natural language processing (Computer science) |
| ISBN | 3-031-17120-9 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
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 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.
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). 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 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. 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. 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 and Results -- 5.1 Data-set -- 5.2 Experimental Setup -- 5.3 Main Results -- 5.4 Ablation Study and Qualitative Analysis -- 6 Conclusion -- References. Dual Interactive Attention Network for Joint Entity and Relation Extraction. |
| Record Nr. | UNISA-996490354703316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Natural Language Processing and Chinese Computing : 9th CCF International Conference, NLPCC 2020, Zhengzhou, China, October 14-18, 2020, Proceedings, Part II / / Xiaodan Zhu [and three others], editors
| Natural Language Processing and Chinese Computing : 9th CCF International Conference, NLPCC 2020, Zhengzhou, China, October 14-18, 2020, Proceedings, Part II / / Xiaodan Zhu [and three others], editors |
| Edizione | [1st ed. 2020.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2020] |
| Descrizione fisica | 1 online resource (XXX, 591 p. 317 illus., 146 illus. in color.) |
| Disciplina | 006.35 |
| Collana | Lecture Notes in Artificial Intelligence |
| Soggetto topico |
Natural language processing (Computer science)
Chinese language - Data processing |
| ISBN | 3-030-60457-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Trending Topics (Explainability, Ethics, Privacy, Multimodal NLP) -- Poster -- Explainable AI Workshop -- Student Workshop -- Evaluation Workshop. |
| Record Nr. | UNISA-996418289103316 |
| Cham, Switzerland : , : Springer, , [2020] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
