LEADER 06022nam 22006735 450 001 9910647777103321 005 20251113184554.0 010 $a9783031247552 010 $a3031247558 024 7 $a10.1007/978-3-031-24755-2 035 $a(MiAaPQ)EBC7191156 035 $a(Au-PeEL)EBL7191156 035 $a(CKB)26089744900041 035 $a(DE-He213)978-3-031-24755-2 035 $a(PPN)268204446 035 $a(EXLCZ)9926089744900041 100 $a20230202d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aInformation Retrieval $e28th China Conference, CCIR 2022, Chongqing, China, September 16?18, 2022, Revised Selected Papers /$fedited by Yi Chang, Xiaofei Zhu 205 $a1st ed. 2023. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2023. 215 $a1 online resource (117 pages) 225 1 $aLecture Notes in Computer Science,$x1611-3349 ;$v13819 311 08$aPrint version: Chang, Yi Information Retrieval Cham : Springer,c2023 9783031247545 327 $aIntro -- Preface -- Organization -- Contents -- A Position-Aware Word-Level and Clause-Level Attention Network for Emotion Cause Recognition -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 The Definition of Emotion Cause Recognition -- 3.2 Position-Aware Word-Level and Clause-Level Attention Network for Emotion Cause Recognition -- 3.3 Model Training -- 4 Experiment -- 4.1 Experimental Settings -- 4.2 Experimental Results -- 4.3 Qualitative Analysis -- 5 Conclusion and Future Work -- References -- ID-Agnostic User Behavior Pre-training for Sequential Recommendation -- 1 Introduction -- 2 Preliminaries -- 3 Methodology -- 3.1 ID-Agnostic User Behavior Pre-training -- 3.2 Fine-Tuning for Recommendation -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Experimental Results -- 5 Related Work -- 6 Conclusion -- References -- Enhance Performance of Ad-hoc Search via Prompt Learning -- 1 Introduction -- 2 Related Work -- 2.1 Ad Hoc Search with PTM -- 2.2 Prompt Learning -- 3 Preliminary -- 3.1 Ad hoc Search -- 3.2 Prompt Learning -- 4 Methodology -- 5 Experiments -- 5.1 Dataset and Metric -- 5.2 Experimental Setup -- 5.3 Result and Analysis -- 5.4 Case Study -- 6 Conclusion -- References -- Syntax-Aware Transformer for Sentence Classification -- 1 Introduction -- 2 Syntax-Aware Transformer -- 2.1 Syntactic Subnetwork -- 2.2 Semantic Subnetwork -- 2.3 Merging Layer -- 3 Experiments -- 3.1 Datasets -- 3.2 Experimental Settings -- 3.3 Baseline Models -- 3.4 Results and Discussion -- 3.5 Case Study -- 4 Conclusions -- References -- Evaluation of Deep Reinforcement Learning Based Stock Trading -- 1 Introduction -- 2 Related Works -- 3 RL Modeling of Stock Trading -- 3.1 Problem Description -- 3.2 Mathematical Presentation -- 3.3 Trading Details -- 3.4 Feasibility Analysis of RL-Based Stock Trading -- 4 Experiments -- 4.1 Stock Dataset. 327 $a4.2 Methodology -- 4.3 Results -- 5 Conclusion and Future Works -- References -- InDNI: An Infection Time Independent Method for Diffusion Network Inference -- 1 Introduction -- 2 Related Work -- 3 Problem Statement -- 4 InDNI Algorithm -- 4.1 Node Representation Learning -- 4.2 Similarity Measure -- 4.3 Filtering Candidate Node Pairs -- 4.4 Network Inference -- 5 Experiments -- 5.1 Experimental Setup -- 5.2 Results and Discussion -- 6 Conclusion and Future Work -- References -- Beyond Precision: A Study on Recall of Initial Retrieval with Neural Representations -- 1 Introduction -- 2 Related Work -- 2.1 Initial Retrieval -- 2.2 Neural Representations for IR -- 3 Our Approach -- 3.1 Symbolic Index -- 3.2 Neural Index -- 3.3 Parallel Search Scheme -- 3.4 Sequential Search Scheme -- 3.5 Discussions -- 4 Experiments -- 4.1 Baselines and Experimental Settings -- 4.2 Evaluation Methodology -- 4.3 Retrieval Performance and Analysis -- 4.4 Analysis on Retrieved Relevant Documents -- 5 Conclusions -- References -- A Learnable Graph Convolutional Neural Network Model for Relation Extraction -- 1 Introduction -- 2 Related Work -- 3 Model -- 3.1 Input Representation Layer -- 3.2 Fusion Module -- 3.3 Classification Module -- 4 Experiments -- 4.1 Datasets -- 4.2 Hyper-parameter Setting -- 4.3 Overall Performance -- 4.4 Ablation Study -- 4.5 Effect of Length of Each Part -- 5 Conclusion and Future Work -- References -- Author Index. 330 $aThis book constitutes the refereed proceedings of the 28th China Conference on Information Retrieval, CCIR 2022, held in Chongqing, China, in September 2022. Information retrieval aims to meet the demand of human on the Internet to obtain information quickly and accurately. The 8 full papers presented were carefully reviewed and selected from numerous submissions. The papers provide a wide range of research results in information retrieval area. 410 0$aLecture Notes in Computer Science,$x1611-3349 ;$v13819 606 $aInformation storage and retrieval systems 606 $aApplication software 606 $aData mining 606 $aArtificial intelligence 606 $aInformation Storage and Retrieval 606 $aComputer and Information Systems Applications 606 $aData Mining and Knowledge Discovery 606 $aArtificial Intelligence 615 0$aInformation storage and retrieval systems. 615 0$aApplication software. 615 0$aData mining. 615 0$aArtificial intelligence. 615 14$aInformation Storage and Retrieval. 615 24$aComputer and Information Systems Applications. 615 24$aData Mining and Knowledge Discovery. 615 24$aArtificial Intelligence. 676 $a025.04 676 $a025.04 702 $aZhu$b Xiaofei 702 $aChang$b Yi 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910647777103321 996 $aInformation retrieval$9331159 997 $aUNINA