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001440809 019__ $$a1287768372$$a1292518826
001440809 020__ $$a9783030893637$$q(electronic bk.)
001440809 020__ $$a3030893634$$q(electronic bk.)
001440809 020__ $$z9783030893620
001440809 0247_ $$a10.1007/978-3-030-89363-7$$2doi
001440809 035__ $$aSP(OCoLC)1283854774
001440809 040__ $$aEBLCP$$beng$$erda$$epn$$cEBLCP$$dGW5XE$$dDCT$$dOCLCF$$dDKU$$dOCLCO$$dOCLCQ$$dCOM$$dOCLCO$$dOCLCQ
001440809 049__ $$aISEA
001440809 050_4 $$aQ334$$b.P33 2021
001440809 08204 $$a006.3$$223
001440809 1112_ $$aPacific Rim International Conference on Artificial Intelligence$$n(18th :$$d2021 :$$cOnline)
001440809 24510 $$aPRICAI 2021 : trends in artificial intelligence :$$b18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021, Hanoi, Vietnam, November 8-12, 2021 : proceedings.$$nPart II /$$cDuc Nghia Pham, Thanaruk Theeramunkong, Guido Governatori, Fenrong Liu (eds.).
001440809 264_1 $$aCham :$$bSpringer,$$c[2021]
001440809 264_4 $$c©2021
001440809 300__ $$a1 online resource (628 pages) :$$billustrations (some color)
001440809 336__ $$atext$$btxt$$2rdacontent
001440809 337__ $$acomputer$$bc$$2rdamedia
001440809 338__ $$aonline resource$$bcr$$2rdacarrier
001440809 347__ $$atext file
001440809 347__ $$bPDF
001440809 4901_ $$aLecture notes in computer science. Lecture notes in artificial intelligence ;$$v13032
001440809 4901_ $$aLNCS sublibrary: SL7 - Artificial intelligence
001440809 500__ $$aInternational conference proceedings.
001440809 500__ $$aConference held in a virtual format.
001440809 500__ $$aIncludes author index.
001440809 5058_ $$aIntro -- Preface -- Organization -- Contents -- Part II -- Natural Language Processing -- A Calibration Method for Sentiment Time Series by Deep Clustering -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Sentence Embedding -- 3.2 Representative Sampling -- 3.3 Sentiment Score Calibration -- 4 Experiment -- 4.1 Dataset -- 4.2 Baselines -- 4.3 Experimental Settings -- 4.4 Evaluation Metrics -- 4.5 Analysis of the Parameter Cluster Number -- 4.6 Compare with Random Sampling -- 5 Conclusion -- References -- A Weak Supervision Approach with Adversarial Training for Named Entity Recognition
001440809 5058_ $$a1 Introduction -- 2 Related Work -- 3 Approach -- 3.1 Labeling Functions -- 3.2 WSAT: Weak Supervision Approach with Adversarial Training -- 4 Experimental Results -- 4.1 Dataset -- 4.2 Baselines -- 4.3 Results and Discussion -- 5 Conclusion -- References -- An Attention-Based Approach to Accelerating Sequence Generative Adversarial Nets -- 1 Introduction -- 2 Related Work -- 3 Our Approach -- 3.1 Attention-Based Discriminator -- 3.2 Attention to Rewards -- 3.3 Training of G -- 4 Experiments -- 4.1 Training Settings -- 4.2 Baselines -- 4.3 Evaluation Metrics -- 4.4 Synthetic Data Experiments
001440809 5058_ $$a4.5 Dialogue Generation: DailyDialog -- 4.6 Internal Comparison Experiments -- 5 Conclusion and Future Work -- References -- Autoregressive Pre-training Model-Assisted Low-Resource Neural Machine Translation -- 1 Introduction -- 2 Background -- 3 Method -- 3.1 Partial Factorization Sequence Acquisition -- 3.2 NMT Model Integrated with Autoregressive Based XLNet -- 3.3 Knowledge Distillation Method -- 4 Experiments -- 4.1 Results and Analysis -- 4.2 Ablation Experiments -- 4.3 Case Study -- 5 Conclusion -- References
001440809 5058_ $$aCombining Improvements for Exploiting Dependency Trees in Neural Semantic Parsing -- 1 Introduction -- 2 Related Work -- 3 Three Improvements -- 3.1 Parent-Scaled Self-attention (PASCAL) -- 3.2 Syntax-Aware Word Representations (SAWRs) -- 3.3 Constituent Attention (CA) -- 4 Combining Improvements -- 5 Experiments -- 5.1 Datasets -- 5.2 Evaluation Metrics -- 5.3 Implementation Details -- 5.4 Results -- 5.5 Visual Analysis -- 6 Conclusion -- References -- Deep Semantic Fusion Representation Based on Special Mechanism of Information Transmission for Joint Entity-Relation Extraction -- 1 Introduction
001440809 5058_ $$a2 Related Work -- 3 Task Definition and Tagging Scheme -- 4 The Proposed Model -- 4.1 Representations of Token and Relation -- 4.2 Deep Semantics Fusion -- 4.3 Triple Extraction -- 4.4 Training -- 5 Experiments -- 5.1 Dataset and Experimental Settings -- 5.2 Baselines and Evaluation Metrics -- 5.3 Experimental Results -- 6 Analysis -- 6.1 Ablation Study -- 6.2 Parameter Analysis -- 6.3 Analysis on Different Sentence Types -- 7 Conclusion -- References -- Exploiting News Article Structure for Automatic Corpus Generation of Entailment Datasets -- 1 Introduction -- 2 Methodology -- 2.1 NLI Datasets from News Articles.
001440809 506__ $$aAccess limited to authorized users.
001440809 520__ $$aThis three-volume set, LNAI 13031, LNAI 13032, and LNAI 13033 constitutes the thoroughly refereed proceedings of the 18th Pacific Rim Conference on Artificial Intelligence, PRICAI 2021, held in Hanoi, Vietnam, in November 2021. The 93 full papers and 28 short papers presented in these volumes were carefully reviewed and selected from 382 submissions. PRICAI covers a wide range of topics in the areas of social and economic importance for countries in the Pacific Rim: artificial intelligence, machine learning, natural language processing, knowledge representation and reasoning, planning and scheduling, computer vision, distributed artificial intelligence, search methodologies, etc. Part II includes two thematic blocks: Natural Language Processing, followed by Neural Networks and Deep Learning.
001440809 588__ $$aDescription based on print version record.
001440809 650_0 $$aArtificial intelligence$$vCongresses.
001440809 650_6 $$aIntelligence artificielle$$vCongrès.
001440809 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001440809 655_7 $$aConference papers and proceedings.$$2lcgft
001440809 655_7 $$aActes de congrès.$$2rvmgf
001440809 655_0 $$aElectronic books.
001440809 7001_ $$aPham, Duc-Nghia,$$eeditor.
001440809 7001_ $$aTheeramunkong, Thanaruk,$$eeditor.
001440809 7001_ $$aGovernatori, Guido,$$eeditor.
001440809 7001_ $$aLiu, Fenrong,$$eeditor.
001440809 77608 $$iPrint version:$$aPham, Duc Nghia.$$tPRICAI 2021: Trends in Artificial Intelligence.$$dCham : Springer International Publishing AG, ©2021$$z9783030893620
001440809 830_0 $$aLecture notes in computer science.$$pLecture notes in artificial intelligence.
001440809 830_0 $$aLecture notes in computer science ;$$v13032.
001440809 830_0 $$aLNCS sublibrary.$$nSL 7,$$pArtificial intelligence.
001440809 852__ $$bebk
001440809 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-030-89363-7$$zOnline Access$$91397441.1
001440809 909CO $$ooai:library.usi.edu:1440809$$pGLOBAL_SET
001440809 980__ $$aBIB
001440809 980__ $$aEBOOK
001440809 982__ $$aEbook
001440809 983__ $$aOnline
001440809 994__ $$a92$$bISE