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MARC Lista (tabellare)
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
Opac: Controlla la disponibilità qui
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. UNINA-9910746284403321
Singapore : , : Springer, , 2023
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
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. UNINA-9910495165503321
Cham, Switzerland : , : Springer, , [2021]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
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
Opac: Controlla la disponibilità qui
Chinese Language Resources : Data Collection, Linguistic Analysis, Annotation and Language Processing / / Chu-Ren Huang, Shu-Kai Hsieh, and Peng Jin, editors
Chinese Language Resources : Data Collection, Linguistic Analysis, Annotation and Language Processing / / Chu-Ren Huang, Shu-Kai Hsieh, and Peng Jin, editors
Edizione [First edition.]
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2023]
Descrizione fisica 1 online resource (0 pages)
Disciplina 495.1072
Collana Text, Speech and Language Technology Series
Soggetto topico Chinese language - Data processing
Computational linguistics
ISBN 3-031-38913-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Biography of Prof. Shiwen Yu -- A Chronological Biography of Professor Shiwen Yu -- Acknowledgments -- Contents -- Editors and Contributors -- Part I: Overview -- Chapter 1: Chinese Language Resources Through One-Third of a Century -- 1.1 Headwater -- 1.2 Vision: Of Peaks and Giants -- 1.3 From the Great Mountains Long Streams Flow -- 1.4 The Versatility of Language Resources 上 -- 1.5 Giving Shape to Water -- 1.6 Deriving Sharable and Versatile Knowledge 不 -- 1.7 The Power of Language Data as Water 然 -- 1.8 Conclusion and Dedication 有 -- References -- Chapter 2: Chinese Comprehensive Language Knowledge Base -- 2.1 Why Was the Chinese Language Knowledge Base Constructed? -- 2.2 Cornerstone of the CLKB: Grammatical Knowledge Base of Contemporary Chinese -- 2.3 Profile of the CLKB -- 2.3.1 PSKB -- 2.3.2 BPTC -- 2.4 What Was Learned from the Development of the CLKB? -- 2.4.1 Fundamental Research and Application Research -- 2.4.2 Theoretical Research and Engineering Practices -- 2.4.3 Development Goals and Process Monitoring -- 2.4.4 Balance of Scale and Quality -- 2.5 Conclusion -- References -- Chapter 3: Introduction to CKIP´s Language Resources and Their Applications -- 3.1 Background -- 3.2 Language Resources -- 3.2.1 Chinese Writing System Resources -- Database of Component Parts of Chinese Characters -- Hantology -- 3.2.2 Lexical Databases and Grammar -- CKIP Lexical Knowledge Base -- Information-Based Case Grammar -- 3.2.3 Corpora -- Sinica Chinese Corpus -- Sinica Ancient Chinese Corpus -- Sinica Treebank -- Language Resources Derived from the Sinica Corpus -- 3.2.4 WordNet and Ontologies -- Bilingual Ontological WordNet -- Chinese WordNet -- Extended-HowNet -- 3.2.5 Integrated Resources -- Chinese Sketch Engine -- 3.3 Core Tools in Chinese Language Processing -- 3.3.1 Word Segmentation and POS Tagging.
3.3.2 Part-of-Speech Tagging -- 3.3.3 Parsing -- 3.3.4 Automatic Semantic Role Assignment -- 3.4 Applications of CKIP´s Resources -- 3.4.1 Word Segmentation and Part-of-Speech Tagging Using YamCha and CRF++ -- 3.4.2 Viterbi PCFG Parser, Syntactic Complexity, and Chinese Readability -- 3.4.3 Chinese Dependency Parser -- 3.4.4 Chinese Dependency Relations Database and Lexicology -- 3.4.5 Selectional Preferences -- 3.4.6 Unsupervised and Minimally Supervised Approaches to Word Sense Disambiguation -- 3.4.7 Vector Semantics and Deep Neural Net -- 3.5 Conclusion: Interdisciplinary Impact and Future Research -- References -- Part II: Language Resources: Annotation and Processing -- Chapter 4: Practical and Robust Chinese Word Segmentation and PoS Tagging -- 4.1 Introduction -- 4.2 Word Boundary Detection Model Robust Word Segmentation -- 4.2.1 From Word Identification to Boundary Decision -- 4.2.2 Word Boundary Decision (WBD) -- 4.3 From Online Learning to Active Learning -- 4.3.1 Online Semi-supervised Learning with Labeled Data -- 4.3.2 Online Semi-supervised Learning with Unlabeled Data -- 4.3.3 Performances of WBD -- 4.3.4 Results of Online Learning with Unlabeled Data -- 4.3.5 Active Learning Approach for CWS: Meeting the Three Challenges -- 4.4 Robustness of PoS Tagging and Quality Assurance: A Two-Tagset Model -- 4.4.1 Corpus-Based POS Tag Mapping -- 4.5 Linguistic Ramification -- 4.6 Conclusion: The Convergence of Linguistic and Stochastic Modeling -- References -- Chapter 5: Describing the Grammatical Knowledge of Chinese Words for Natural Language Processing -- 5.1 Introduction -- 5.2 Overall Design of the Knowledge Base -- 5.2.1 Databases -- 5.2.2 Selection of Words -- 5.3 Classification of Words -- 5.3.1 Basic Word Classes -- 5.3.2 Purpose of Word Classification -- 5.3.3 Word Class Definitions by Grammatical Functions.
5.3.4 Multi-class Words, Homographs, and Homonyms -- 5.4 Description of Grammatical Properties -- 5.4.1 Selection of Grammatical Attributes -- Morphological Attributes -- Syntactic Attributes -- Semantic Attributes -- Collocation -- 5.4.2 Data Redundancy -- 5.4.3 Value Types -- 5.5 Semantic Considerations in the GKB -- 5.5.1 Word Entries Distinguished by Their Meanings -- 5.5.2 Semantic Properties Described for Word Entries -- 5.5.3 Grammatical Properties Distinguished Based on Semantic Clues -- 5.6 Conclusion -- References -- Chapter 6: DeepLEX -- 6.1 Introduction -- 6.2 Current Approaches and Issues -- 6.2.1 Chinese Lexical Resources -- 6.3 DeepLEX -- 6.3.1 Modules -- 6.3.2 Fluid Annotation -- 6.4 Conclusion -- References -- Chapter 7: The Chinese Generalized Function Word Usage Knowledge Base and Its Applications -- 7.1 Introduction -- 7.2 Chinese Function Word Usage Knowledge Base -- 7.2.1 Framework and Construction Process of the CFKB -- 7.2.2 Function Word Usage Dictionary -- 7.2.3 Function Word Usage Rule Base -- 7.2.4 Function Word Usage Corpus -- 7.3 Automatic Identification of Function Word Usages -- 7.3.1 Rule-Based Method -- 7.3.2 Statistics-Based Method -- 7.3.3 Combined Rule-Based/Statistics-Based Method -- 7.4 Applications Based on the CFKB -- 7.4.1 Syntactic Analysis -- 7.4.2 Grammar Error Analysis -- 7.4.3 Information Extraction -- 7.4.4 Chinese Deep Semantic Understanding -- 7.5 Conclusion -- References -- Chapter 8: A Generic Study of Linguistic Information Based on the Chinese Idiom Knowledge Base and Its Expansion -- 8.1 Introduction -- 8.2 Construction, Structure, and Properties of the Knowledge Base for Chinese Idiomatic Expressions -- 8.3 Nature, Living, History, Culture, and Idiomatic Expressions -- 8.4 Conclusion -- References -- Chapter 9: Lexical Knowledge Representation and Semantic Composition of E-HowNet.
9.1 Introduction -- 9.2 Overview of E-HowNet -- 9.2.1 Ontology of Concepts -- 9.3 Lexical Knowledge Representation and Semantic Composition -- 9.3.1 Principles of Sense Definitions -- 9.3.2 Uniform Representation of Content Words and Function Words for Semantic Composition -- 9.3.3 Basic Composition Process -- 9.4 Semantic Role Labeling -- 9.4.1 Establishing a Reasonable Set of Semantic Roles -- 9.4.2 Guidelines for Pursuing Role Assignment -- 9.4.3 Difficulties and Solutions -- 9.5 Conclusion and Future Work -- References -- Chapter 10: Sense Tagging Unknown Chinese Words with Word Embedding -- 10.1 Introduction -- 10.2 Related Work -- 10.3 Linguistic Features of Unknown Words -- 10.3.1 Paradigmatic Features of Words -- 10.3.2 Features of Chinese Word Formation -- 10.4 Introduction of the Semantic Resource -- 10.5 Model Construction -- 10.5.1 Model Based on Word Embedding -- 10.5.2 Combined Model Based on Word Embedding and POS Filtering -- 10.5.3 Model Based on Word Embedding, POS Filtering, and Suffix Filtering -- 10.6 Experiments -- 10.6.1 Evaluation Metrics -- 10.6.2 Experimental Setting -- 10.6.3 Experiments and Analysis -- Setting the Size of Related Words´´ K Values -- Results and Analysis -- 10.7 Multimodel Cascade -- 10.8 Conclusion -- References -- Chapter 11: PKUSenseCor: A Large-Scale Word Sense Annotated Chinese Corpus -- 11.1 Introduction -- 11.2 Corpus and Knowledge Base Selection -- 11.2.1 Corpus -- 11.2.2 Sense Inventory: The Grammatical Knowledge Base of Contemporary Chinese -- 11.3 Corpus Annotation -- 11.3.1 The Annotation Process -- 11.4 Inter-annotator Agreement -- 11.5 Conclusion -- References -- Chapter 12: Semantic Annotation and Mandarin VerbNet -- 12.1 Introduction -- 12.2 Issues in the Annotation of Chinese Verbs -- 12.3 Frame-Based Constructional Approach to Semantic Annotation -- 12.3.1 Emotion Archi-frames.
12.3.2 Motion Archi-frames -- 12.4 Advantages of the Approach -- 12.5 Conclusion -- References -- Chapter 13: The Construction of a Chinese Semantic Dependency Graph Bank -- 13.1 Introduction -- 13.2 Annotation Scheme of the Semantic Dependency Graph -- 13.2.1 Graph Structure of Semantic Dependency -- 13.2.2 Semantic Relation Set -- 13.2.3 Special Situations -- 13.3 Corpus -- 13.3.1 Corpus Origin -- 13.3.2 Annotation Tool -- 13.4 Evaluation of the Corpus -- 13.5 Corpus Statistics -- 13.6 Conclusion -- References -- Chapter 14: A Chinese Dialogue Corpus Annotated with Dialogue Act -- 14.1 Introduction -- 14.2 Related Work -- 14.2.1 Existing Datasets -- 14.2.2 Traditional Methods -- 14.2.3 Deep Learning Models -- 14.3 Annotation of a Group Chat Corpus -- 14.3.1 Data Collection and Preprocessing -- 14.3.2 Annotation Specification -- Dimension 1 (D1): Semantic Information -- Dimension 2 (D2): Reaction to Context -- Dimension 3 (D3): Effect of Turn-Taking on Topic -- 14.3.3 Annotated Example -- 14.3.4 Consistency Check -- 14.3.5 Dataset Statistics -- 14.3.6 Confusion Matrix -- 14.4 Baseline Methods -- 14.4.1 Dataset -- 14.4.2 Evaluation Metrics -- 14.4.3 Conditional Random Field (CRF) Model -- 14.4.4 Recurrent Neural Networks (RNN) -- 14.5 Experimental Results -- 14.5.1 Human Performance -- 14.5.2 Model Performance -- Random Selection -- CRF Model -- RNN Model -- 14.6 Conclusions -- References -- Chapter 15: Automatic Construction of Parallel Dialogue Corpora with Rich Information -- 15.1 Introduction -- 15.2 Related Work -- 15.3 Building a Parallel Dialogue Corpus -- 15.3.1 Script and Subtitle -- 15.3.2 Movie Alignment -- 15.3.3 Sentence Alignment -- 15.4 Experiments and Results -- 15.4.1 Parallel Dialogue Corpus Construction -- 15.4.2 Improved Translation with Speaker Information -- 15.5 Conclusion and Future Work -- References.
Chapter 16: A Chinese Event-Based Emotion Corpus: Emotion Cause Detection.
Record Nr. UNINA-9910770258703321
Cham, Switzerland : , : Springer, , [2023]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
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  
Hackensack, NJ, : World Scientific, c2006
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
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
Dong Zhendong  
Hackensack, NJ, : World Scientific, c2006
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
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-9910807913503321
Dong Zhendong  
Hackensack, NJ, : World Scientific, c2006
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
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
Opac: Controlla la disponibilità qui
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. UNINA-9910595031503321
Cham, Switzerland : , : Springer, , [2022]
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