LEADER 05488nam 2200481 450 001 9910627261503321 005 20231110212848.0 010 $a981-19-5391-0 035 $a(MiAaPQ)EBC7118619 035 $a(Au-PeEL)EBL7118619 035 $a(CKB)25171049000041 035 $a(PPN)265856299 035 $a(EXLCZ)9925171049000041 100 $a20230304d2023 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aInvestigations in entity relationship extraction /$fSachin Sharad Pawar, Pushpak Bhattacharyya, Girish Keshav Palshikar 210 1$aSingapore :$cSpringer,$d[2023] 210 4$d©2023 215 $a1 online resource (156 pages) 225 1 $aStudies in Computational Intelligence ;$vv.1058 311 08$aPrint version: Pawar, Sachin Sharad Investigations in Entity Relationship Extraction Singapore : Springer,c2022 9789811953903 320 $aIncludes bibliographical references. 327 $aIntro -- Preface -- Acknowledgements -- Contents -- About the Authors -- 1 Introduction -- 1.1 Entities -- 1.2 Relations -- 1.2.1 Global- versus Mention-level Relations -- 1.3 Motivation -- 1.4 Research Gaps and Objectives -- 1.4.1 End-to-end Relation Extraction -- 1.4.2 N-ary Cross-sentence Relation Extraction -- 1.5 Organization of the Monograph -- References -- 2 Literature Survey -- 2.1 Relation Extraction -- 2.1.1 Feature-based Methods -- 2.1.2 Kernel Methods -- 2.1.3 Neural Approaches -- 2.1.4 Datasets -- 2.1.5 Evaluation -- 2.2 Joint Entity and Relation Extraction -- 2.2.1 Motivating Example -- 2.2.2 Overview of Techniques -- 2.2.3 Joint Inference Techniques -- 2.2.4 Joint Models -- 2.2.5 Experimental Evaluation -- 2.3 N-ary Cross-sentence Relation Extraction -- 2.3.1 Extracting Cross-sentence Relations -- 2.3.2 Extracting N-ary and Cross-sentence Relations -- References -- 3 Joint Inference for End-to-end Relation Extraction -- 3.1 Introduction -- 3.1.1 Problem Definition -- 3.1.2 Motivation for Joint Extraction -- 3.2 Background: Markov Logic Networks (MLN) -- 3.2.1 Basics of First-order Logic -- 3.2.2 Basics of MLNs -- 3.2.3 Formal Definition -- 3.2.4 Inference in MLNs -- 3.3 Building Blocks for Our Approach -- 3.3.1 Identifying Entity Mention Candidates -- 3.3.2 Entity Type Classifier -- 3.3.3 Entity Type Agnostic Relation Classifier -- 3.3.4 Pipeline Relation Classifier -- 3.4 Joint Extraction using Inference in Markov Logic Networks (MLN) -- 3.4.1 Motivation -- 3.4.2 Domains and Predicates -- 3.4.3 Generic Rules -- 3.4.4 Sentence-specific Rules -- 3.4.5 Additional Semantic Rules -- 3.4.6 Joint Inference -- 3.5 Example -- 3.6 Experimental Analysis -- 3.6.1 Limitations of Our Approach -- References -- 4 Joint Model for End-to-End Relation Extraction -- 4.1 Motivation -- 4.2 All Word Pairs Model (AWP-NN). 327 $a4.2.1 Features for the AWP-NN Model -- 4.2.2 Architecture of the AWP-NN Model -- 4.3 Inference Using Markov Logic Networks -- 4.4 Experimental Analysis -- 4.4.1 Datasets -- 4.4.2 Implementation Details -- 4.4.3 Results -- 4.4.4 Analysis of Results -- 4.5 Domain-Specific Entities and Relations -- 4.5.1 Adverse Drug Reactions -- 4.5.2 TAC 2017: ADR Extraction Task -- References -- 5 N-ary Cross-Sentence Relation Extraction -- 5.1 Introduction -- 5.2 Problem Definition -- 5.2.1 Comparison with Relevant Past Work -- 5.3 Proposed Approach -- 5.3.1 Constructing Sequence Representations -- 5.3.2 Constrained Subsequence Kernel (CSK) -- 5.3.3 Formal Definition of CSK -- 5.3.4 Classifying Candidate Relation Instances -- 5.4 Experimental Analysis -- 5.4.1 Datasets -- 5.4.2 Implementation Details -- 5.4.3 Analysis of Results and Errors -- 5.5 Discussion on Decomposition of N-ary Relations -- 5.5.1 Examples of Various Relation Types -- 5.5.2 Generalized Theorem -- References -- 6 Recent Advances in Entity and Relation Extraction -- 6.1 Joint Entity and Relation Extraction -- 6.1.1 Using Span-Based Representation for Entity Mentions -- 6.1.2 Using BERT Embeddings -- 6.2 N-ary Cross-Sentence Relation Extraction -- 6.2.1 Standard Dataset -- 6.2.2 Graph Neural Networks -- 6.2.3 Using BERT Embeddings -- References -- 7 Conclusions -- 7.1 Summary of the Monograph -- 7.2 Future Directions -- References -- Appendix A Foundations -- A.1 Maximum Entropy Classifier -- A.2 Conditional Random Fields (CRF) -- A.3 Markov Logic Networks (MLN) -- A.3.1 Basics of First-order Logic -- A.3.2 Basics of MLNs -- A.3.3 Formal Definition -- A.3.4 Example -- A.3.5 Inference in MLNs -- A.4 Neural Networks -- A.4.1 Layers in Neural Networks -- A.4.1.1 Embedding Layer (Input Layer) -- A.4.1.2 Linear Layer -- A.4.1.3 Softmax Layer (Output Layer) -- A.4.2 Training Neural Networks. 327 $aA.4.3 Long Short-Term Memory Networks (LSTM) -- A.5 Support Vector Machines (SVM) with Kernels -- A.5.1 Kernel Functions -- A.5.2 String Subsequence Kernel -- A.5.3 Generalized Subsequence Kernel -- References. 410 0$aStudies in Computational Intelligence 606 $aExtraction (Linguistics) 615 0$aExtraction (Linguistics) 676 $a929.374 700 $aPawar$b Sachin Sharad$01267603 702 $aBhattacharyya$b Pushpak 702 $aPalshikar$b Girish Keshav 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910627261503321 996 $aInvestigations in Entity Relationship Extraction$92981730 997 $aUNINA LEADER 05688nam 2200757Ia 450 001 9910816078203321 005 20240514074809.0 010 $a9783527639519 010 $a3527639519 010 $a9781283869713 010 $a1283869713 010 $a9783527639526 010 $a3527639527 010 $a9783527639502 010 $a3527639500 035 $a(CKB)2670000000177374 035 $a(EBL)822730 035 $a(OCoLC)792684103 035 $a(SSID)ssj0000632004 035 $a(PQKBManifestationID)11374052 035 $a(PQKBTitleCode)TC0000632004 035 $a(PQKBWorkID)10610264 035 $a(PQKB)11252581 035 $a(MiAaPQ)EBC822730 035 $a(Au-PeEL)EBL822730 035 $a(CaPaEBR)ebr10577528 035 $a(CaONFJC)MIL418221 035 $a(PPN)243140800 035 $a(Perlego)1002461 035 $a(EXLCZ)992670000000177374 100 $a20091027d2012 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 00$aAnion coordination chemistry /$fedited by Kristin Bowman-James, Antonio Bianchi, Enrique Garci?a-Espana 205 $a1st ed. 210 $aWeinheim $cWiley-VCH$dc2012 215 $a1 online resource (575 p.) 300 $aDescription based upon print version of record. 311 08$a9783527323708 311 08$a3527323708 320 $aIncludes bibliographical references and index. 327 $aAnion Coordination Chemistry; Contents; Preface; List of Contributors; 1 Aspects of Anion Coordination from Historical Perspectives; 1.1 Introduction; 1.2 Halide and Pseudohalide Anions; 1.3 Oxoanions; 1.4 Phosphate and Polyphosphate Anions; 1.5 Carboxylate Anions and Amino Acids; 1.6 Anionic Complexes: Supercomplex Formation; 1.7 Nucleotides; 1.8 Final Notes; References; 2 Thermodynamic Aspects of Anion Coordination; 2.1 Introduction; 2.2 Parameters Determining the Stability of Anion Complexes; 2.2.1 Type of Binding Group: Noncovalent Forces in Anion Coordination 327 $a2.2.2 Charge of Anions and Receptors2.2.3 Number of Binding Groups; 2.2.3.1 Additivity of Noncovalent Forces; 2.2.4 Preorganization; 2.2.4.1 Macrocyclic Effect; 2.2.5 Solvent Effects; 2.3 Molecular Recognition and Selectivity; 2.4 Enthalpic and Entropic Contributions in Anion Coordination; References; 3 Structural Aspects of Anion Coordination Chemistry; 3.1 Introduction; 3.2 Basic Concepts of Anion Coordination Chemistry; 3.3 Classes of Anion Hosts; 3.4 Acycles; 3.4.1 Bidentate; 3.4.2 Tridentate; 3.4.3 Tetradentate; 3.4.4 Pentadentate; 3.4.5 Hexadentate; 3.5 Monocycles; 3.5.1 Bidentate 327 $a3.5.2 Tridentate3.5.3 Tetradentate; 3.5.4 Pentadentate; 3.5.5 Hexadentate; 3.5.6 Octadentate; 3.5.7 Dodecadentate; 3.6 Cryptands; 3.6.1 Bidentate; 3.6.2 Tridentate; 3.6.3 Tetradentate; 3.6.4 Pentadentate; 3.6.5 Hexadentate; 3.6.6 Septadentate; 3.6.7 Octadentate; 3.6.8 Nonadentate; 3.6.9 Decadentate; 3.6.10 Dodecadentate; 3.7 Transition-Metal-Assisted Ligands; 3.7.1 Bidentate; 3.7.2 Tridentate; 3.7.3 Tetradentate; 3.7.4 Hexadentate; 3.7.5 Septadentate; 3.7.6 Dodecadentate; 3.8 Lewis Acid Ligands; 3.8.1 Transition Metal Cascade Complexes; 3.8.2 Other Lewis Acid Donor Ligands 327 $a3.8.2.1 Boron-Based Ligands3.8.2.2 Tin-Based Ligands; 3.8.2.3 Hg-Based Ligands; 3.9 Conclusion; Acknowledgments; References; 4 Synthetic Strategies; 4.1 Introduction; 4.2 Design and Synthesis of Polyamine-Based Receptors for Anions; 4.2.1 Acyclic Polyamine Receptors; 4.2.2 Tripodal Polyamine Receptors; 4.2.3 Macrocyclic Polyamine Receptors with Aliphatic Skeletons; 4.2.4 Macrocyclic Receptors Incorporating a Single Aromatic Unit; 4.2.5 Macrocyclic Receptors Incorporating Two Aromatic Units; 4.2.6 Anion Receptors Containing Separated Macrocyclic Binding Units; 4.2.7 Cryptands 327 $a4.3 Design and Synthesis of Amide Receptors4.3.1 Acid Halides as Starting Materials; 4.3.1.1 Acyclic Amide Receptors; 4.3.1.2 Macrocyclic Amide Receptors; 4.3.2 Esters as Starting Materials; 4.3.3 Using Coupling Reagents; References; 5 Template Synthesis; 5.1 Introductory Remarks; 5.2 Macrocyclic Systems; 5.3 Bowl-Shaped Systems; 5.4 Capsule, Cage, and Tube-Shaped Systems; 5.5 Circular Helicates and meso-Helicates; 5.6 Mechanically Linked Systems; 5.7 Concluding Remarks; References; 6 Anion-đ Interactions in Molecular Recognition; 6.1 Introduction; 6.2 Physical Nature of the Interaction 327 $a6.3 Energetic and Geometric Features of the Interaction Depending on the Host (Aromatic Moieties) and the Guest (Anions) 330 $aBuilding on the pioneering work in supramolecular chemistry from the last 20 years or so, this monograph addresses new and recentapproaches to anion coordination chemistry. Synthesis of receptors, biological receptors and metallareceptors, the energetics of anion binding, molecular structures of anion complexes, sensing devices are presented and computational studies addressed to aid with the understanding of the different driving forces responsible for anion complexation. The reader is promised an actual picture of the state of the art for this exciting and constantly evolving field of su 606 $aAnions 606 $aSupramolecular chemistry 615 0$aAnions. 615 0$aSupramolecular chemistry. 676 $a541.3722 701 $aBianchi$b Antonio$f1956-$0297266 701 $aBowman-James$b Kristin$f1946-$01720346 701 $aGarci?a-Espan?a$b Enrique$01720347 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910816078203321 996 $aAnion coordination chemistry$94118920 997 $aUNINA