First person singular III [[electronic resource] ] : autobiographies by North American scholars in the language sciences / / edited by E.F.K. Koerner

• Record Nr.: UNINA9910458057203321
• Pubbl/distr/stampa: Amsterdam ; ; Philadelphia, : J. Benjamins, c1998
• ISBN: 1-283-31223-9; 9786613312235; 90-272-7559-9
• Descrizione fisica: 1 online resource (277 p.)
• Collana: Amsterdam studies in the theory and history of linguistic science. Series III, Studies in the history of the language sciences, , 0304-0720 ; ; v. 88
• Altri autori (Persone): KoernerE. F. K
• Disciplina: 410/.92/273; B
• Soggetti: Linguists - United States; Linguistics - United States - History - 20th century; Electronic books.
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di bibliografia: Includes bibliographical references and indexes.
• Nota di contenuto: FIRST PERSON SINGULAR III: AUTOBIOGRAPHIES BY NORTH AMERICAN SCHOLARS IN THE LANGUAGE SCIENCES; Editorial page; Title page; Copyright page; Dedication; EDITOR'S FOREWORD; Table of contents; Location of pictures and photographs:; SIXTY YEARS IN LINGUISTICS; LONG-TERM COMMITMENTS AND LUCKY EVENTS; A PILGRIM'S PROGRESS: FROM PHILOLOGY TO LINGUISTICS; BETWEEN LOGIC AND LINGUISTICS; A LINGUISTIC ROUND TRIP: FROM PRACTICE TO THEORY AND BACK; LINGUISTICS TO THE BEAT OF A DIFFERENT DRUMMER; LINGUISTICS FROM THE BOTTOM UP; A LINGUISTIC PILGRIMAGE; . . .QVEMLEGIS VT NORIS . . .; C'EST PASSIONNANT D'ÊTRE PASSIONNÉFROM ENGLISH PHILOLOGY TO LINGUISTICS AND BACK AGAIN; THE STORY OF A LINGUIST MALGRÉ LUI; INDEXOF BIOGRAPHICAL NAMES; INDEXOF SUBJECTS, TERMS, AND LANGUAGES
• Sommario/riassunto: This sequel to the First Person Singular volumes published in 1980 and ; 1991, respectively (SiHoLS 21 and 61) presents autobiographical accounts by major North American linguists. This material provides an important primary source for the history and development of the discipline during the 20th century. The volume includes photographs of all contributors and is completed by a full index of biographical names and a detailed index of subjects and languages which turn it into a useful research tool.

Case-based reasoning research and development : 30th International Conference, ICCBR 2022, Nancy, France, September 12-15, 2022, proceedings / / Mark T. Keane, Nirmalie Wiratunga (editors)

• Record Nr.: UNISA996485668603316
• Pubbl/distr/stampa: Cham, Switzerland : , : Springer, , [2022]; ©2022
• ISBN: 3-031-14923-8
• Descrizione fisica: 1 online resource (420 pages)
• Collana: Lecture notes in computer science. Lecture notes in artificial intelligence ; ; Volume 13405
• Disciplina: 153.43
• Soggetti: Case-based reasoning; Expert systems (Computer science); Deep learning (Machine learning)
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Intro -- Preface -- Organization -- Invited Talks -- Seeing Through Black Boxes with Human Vision: Deep Learning and Explainable AI in Medical Image Applications -- Case-Based Reasoning for Clinical Decisions That Are Computer-Aided, Not Automated -- Towards More Cognitively Appealing Paradigms in Case-Based Reasoning -- Contents -- Explainability in CBR -- Using Case-Based Reasoning for Capturing Expert Knowledge on Explanation Methods -- 1 Introduction -- 2 Background -- 3 Case-Based Elicitation -- 3.1 Case Structure -- 3.2 Case Base Acquisition -- 4 CBR Process -- 5 Evaluation and Discussion -- 6 Conclusions -- References -- A Few Good Counterfactuals: ; Generating Interpretable, Plausible and Diverse Counterfactual Explanations -- 1 Introduction -- 2 Related Work -- 2.1 What Are Good Counterfactual Explanations? -- 2.2 Perturbation-Based Approaches -- 2.3 Instance-Based Approaches -- 2.4 Instance-Based Shortcomings -- 3 Good Counterfactuals in Multi-class Domains -- 3.1 Reusing the kNN Explanation Cases -- 3.2 Validating Candidate Counterfactuals -- 3.3 Discussion -- 4 Evaluation -- 4.1 Methodology -- 4.2 Results -- 5 Conclusions -- References -- How Close Is Too Close? The Role of Feature Attributions in Discovering Counterfactual Explanations -- 1 Introduction -- 2 Related Work -- 3 DisCERN -- 3.1 Nearest-Unlike Neighbour -- 3.2 Feature Ordering by Feature Attribution -- 3.3 Substitution-Based Adaptation -- 3.4 Integrated Gradients for DisCERN -- 3.5 Bringing the NUN Closer -- 4 Evaluation -- 4.1 Datasets -- 4.2 Experiment Setup -- 4.3 Performance Measures for Counterfactual Explanations -- 5 Results -- 5.1 A Comparison of Feature Attribution Techniques -- 5.2 A Comparison of Counterfactual Discovery Algorithms -- 5.3 Impact of Bringing NUN Closer -- 6 Conclusions -- References -- Algorithmic Bias and Fairness in Case-Based Reasoning.; 1 Introduction -- 2 Related Research -- 2.1 Bias in ML -- 2.2 Bias in CBR -- 2.3 Metric Learning -- 3 FairRet: Eliminating Bias with Metric Learning -- 3.1 Bias and The Similarity Knowledge Container -- 3.2 A Metric Learning Approach -- 3.3 Multi-objective Particle Swarm Optimization -- 4 Results -- 4.1 Dealing with Underestimation Bias -- 4.2 Outcome Distortion -- 4.3 Retrieval Overlap -- 5 Conclusions -- References -- "Better" Counterfactuals, Ones People Can Understand: Psychologically-Plausible Case-Based Counterfactuals Using Categorical Features for Explainable AI (XAI) -- 1 Introduction -- 2 Background: Computation and Psychology of Counterfactuals -- 2.1 User Studies of Counterfactual XAI: Mixed Results -- 3 Study 1: Plotting Counterfactuals that have Categoricals -- 3.1 Results and Discussion -- 4 Transforming Case-Based Counterfactuals, Categorically -- 4.1 Case-Based Counterfactual Methods: CB1-CF and CB2-CF -- 4.2 Counterfactuals with Categorical Transforms #1: Global Binning -- 4.3 Counterfactuals with Categorical Transforms #2: Local Direction -- 5 Study 2: Evaluating CAT-CF Methods -- 5.1 Method: Data and Procedure -- 5.2 Results and Discussion: Counterfactual Distance -- 6 Conclusions -- References -- Representation and Similarity -- Extracting Case Indices from Convolutional Neural Networks: A Comparative Study -- 1 Introduction -- 2 Potential Feature Extraction Points in cnns -- 3 Related Work -- 4 Three Structure-Based Feature Extraction Methods -- 4.1 Post-convolution Feature Extraction -- 4.2 Post-dense Feature Extraction -- 4.3 Multi-net Feature Extraction -- 5 Evaluation -- 5.1 Hypotheses -- 5.2 Test Domain and Test Set Selection -- 5.3 Testbed System -- 5.4 Accuracy Testing and Informal Upper Bound -- 6 Results and Discussion -- 6.1 Comparative Performance -- 6.2 Discussion -- 7 Ramifications for Interpretability.; 8 Conclusions and Future Work -- References -- Exploring the Effect of Recipe Representation on Critique-Based Conversational Recommendation -- 1 Introduction -- 2 Background -- 2.1 Diversity in Recommender Systems -- 2.2 Critique-Based Conversational Recommender Systems -- 2.3 Diversity in Recipe Recommenders -- 3 DiversityBite Framework: Recommend, Review, Revise -- 3.1 Adaptive Diversity Goal Approach -- 4 Evaluation -- 4.1 Case Base -- 4.2 Implementation: DGF, AGD, and Diversity Scoring -- 4.3 Simulation Study: Incorporating Diversity in Critique -- 4.4 User Study: Comparing Different Recipe Representations -- 5 Conclusion -- References -- Explaining CBR Systems Through Retrieval and Similarity Measure Visualizations: A Case Study -- 1 Introduction -- 2 Related Work -- 3 ; SupportPrim CBR System -- 3.1 Data -- 3.2 Case Representation and Similarity Modeling -- 3.3 Case Base and Similarity Population -- 4 Explanatory Case Base Visualizations -- 4.1 Accessing the CBR System's Model -- 4.2 Visualization of Retrievals -- 4.3 Visualization of the Similarity Scores for Individual Case Comparisons -- 5 Experiments -- 6 Discussion -- 7 Conclusion -- References -- Adapting Semantic Similarity Methods for Case-Based Reasoning in the Cloud -- 1 Introduction -- 2 Related Work -- 2.1 Clood CBR -- 2.2 Ontologies in CBR -- 2.3 Retrieval with Word Embedding -- 2.4 Serverless Function Benefits and Limitations -- 3 Semantic Similarity Metrics in a Microservices Architecture -- 3.1 Clood Similarity Functions Overview -- 3.2 Similarity Table -- 3.3 Word Embedding Based Similarity -- 3.4 Ontology-Based Similarity Measure -- 4 Implementation of Semantic Similarity Measures on Clood Framework -- 4.1 Word Embedding Similarity on Clood -- 4.2 Ontology-Based Similarity on Clood -- 5 Evaluation of Resource Impact -- 5.1 Experiment Setup -- 5.2 Result and Discussion.; 6 Conclusion -- References -- Adaptation and Analogical Reasoning -- Case Adaptation with Neural Networks: Capabilities and Limitations -- 1 Introduction -- 2 Background -- 3 NN-CDH for both Classification and Regression -- 3.1 General Model of Case Adaptation -- 3.2 1-Hot/1-Cold Nominal Difference -- 3.3 Neural Network Structure of NN-CDH -- 3.4 Training and Adaptation Procedure -- 4 Evaluation -- 4.1 Systems Being Compared -- 4.2 Assembling Case Pairs for Training -- 4.3 Data Sets -- 4.4 Artificial Data Sets -- 5 Conclusion -- References -- A Deep Learning Approach to Solving Morphological Analogies -- 1 Introduction -- 2 The Problem of Morphological Analogy -- 3 Proposed Approach -- 3.1 Classification, Retrieval and Embedding Models -- 3.2 Training and Evaluation -- 4 Experiments -- 4.1 Data -- 4.2 Refining the Training Procedure -- 4.3 Performance Comparison with State of the Art Methods -- 4.4 Distance of the Expected Result -- 4.5 Case Analysis: Navajo and Georgian -- 5 Conclusion and Perspectives -- References -- Theoretical and Experimental Study of a Complexity Measure for Analogical Transfer -- 1 Introduction -- 2 Reminder on Complexity-Based Analogy -- 2.1 Notations -- 2.2 Ordinal Analogical Principle: Complexity Definition -- 2.3 Ordinal Analogical Inference Algorithm -- 3 Theoretical Property of the Complexity Measure: Upper Bound -- 3.1 General Case -- 3.2 Binary Classification Case -- 4 Algorithmic Optimisation -- 4.1 Principle -- 4.2 Proposed Optimized Algorithm -- 5 Experimental Study -- 5.1 Computational Cost -- 5.2 Correlation Between Case Base Complexity and Performance -- 5.3 Correlation Between Complexity and Task Difficulty -- 6 Conclusion and Future Works -- References -- Graphs and Optimisation -- Case-Based Learning and Reasoning Using Layered Boundary Multigraphs -- 1 Introduction -- 2 Background and Related Work.; 3 Boundary Graphs and Labeled Boundary Multigraphs -- 3.1 Boundary Graphs -- 3.2 Labeled Boundary Multigraphs -- 3.3 Discussion -- 4 Empirical Evaluation -- 4.1 Experimental Set-Up -- 4.2 Classical Benchmark Data Sets -- 4.3 Scaling Analysis -- 5 Conclusion -- References -- Particle Swarm Optimization in Small Case Bases for Software Effort Estimation -- 1 Introduction -- 2 Related Work -- 3 Software Effort Estimation of User Stories -- 4 CBR Approach -- 4.1 Case Representation -- 4.2 Similarity -- 4.3 Adaptation -- 4.4 Weight Optimization with PSO -- 5 Experiments -- 5.1 Experimental Data -- 5.2 Experiment 1 -- 5.3 Experiment 2 -- 5.4 Discussion of Results -- 6 Conclusion -- References -- MicroCBR: Case-Based Reasoning on Spatio-temporal Fault Knowledge Graph for Microservices Troubleshooting -- 1 Introduction -- 2 Related Work -- 3 Background ; and Motivation -- 3.1 Background with Basic Concepts -- 3.2 Motivation -- 4 Troubleshooting Framework -- 4.1 Framework Overview -- 4.2 Spatio-Temporal Fault Knowledge Graph -- 4.3 Fingerprinting the Fault -- 4.4 Case-Based Reasoning -- 5 Evaluation -- 5.1 Evaluation Setup -- 5.2 Q1. Comparative Experiments -- 5.3 Q2. Ablation Experiment -- 5.4 Q3. Efficiency Experiments -- 5.5 Q4. Case Studies and Learned Lessons -- 6 Conclusion -- References -- .26em plus .1em minus .1emGPU-Based Graph Matching for Accelerating Similarity Assessment in Process-Oriented Case-Based Reasoning -- 1 Introduction -- 2 Foundations and Related Work -- 2.1 Semantic Workflow Graph Representation -- 2.2 State-Space Search by Using A* -- 2.3 Related Work -- 3 AMonG: A*-Based Graph Matching on Graphic Processing Units -- 3.1 Overview and Components -- 3.2 Parallel Graph Matching -- 4 Experimental Evaluation -- 4.1 Experimental Setup -- 4.2 Experimental Results -- 4.3 Discussion and Further Considerations -- 5 Conclusion and Future Work.; References.