LEADER 05306nam 2200493 450 001 996511872103316 005 20230508213207.0 010 $a3-031-24755-8 024 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 $a20230508d2023 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aInformation retrieval $e28th China conference, CCIR 2022, Chongqing, China, September 16-18, 2022, revised selected papers /$fYi Chang, Xiaofei Zhu (editors) 205 $a1st ed. 2023. 210 1$aCham, Switzerland :$cSpringer,$d[2023] 210 4$dİ2023 215 $a1 online resource (117 pages) 225 1 $aLecture notes in computer science ;$vVolume 13819 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 ;$vVolume 13819. 606 $aInformation retrieval$vCongresses 615 0$aInformation retrieval 676 $a025.04 702 $aZhu$b Xiaofei 702 $aChang$b Yi 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996511872103316 996 $aInformation retrieval$9331159 997 $aUNISA LEADER 01253nam0 22003133i 450 001 TO10005392 005 20231121125847.0 010 $a9788866270522 100 $a20131003d2012 ||||0itac50 ba 101 | $aita 102 $ait 181 1$6z01$ai $bxxxe 182 1$6z01$an 200 1 $aEnergia da biogas$emanuale per la progettazione, autorizzazione e gestione degli impianti$eprocesso di digestione anaerobica ...$fa cura di Francesco Arecco$gcon Roberto Canziani ... [et. al.] 210 $aMilano$cAmbiente$d[2012] 215 $a210 p.$cill.$d29 cm 225 | $aManuali di progettazione sostenibile 410 0$1001LO11069970$12001 $aManuali di progettazione sostenibile 500 11$aEnergia da biogas.$3MSE0074300$9264918 702 1$aCanziani$b, Roberto$f <1956- >$3MILV269162 702 1$aArecco$b, Francesco$f <1977- $c ; Alessandria>$3RMGV100545 801 3$aIT$bIT-01$c20131003 850 $aIT-FR0099 899 $aBiblioteca Area Ingegneristica$bFR0099 912 $aTO10005392 950 0$aBiblioteca Area Ingegneristica$d 54S. L. 665.7 ENE$e 54SBA0000166205 VMN A4 $fA $h20131003$i20131003 977 $a 54 996 $aEnergia da biogas$9264918 997 $aUNICAS