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Table of Contents
Intro; Preface; Organization; Contents; Query Processing and Retrieval; Mining User Profiles from Query Log; 1 Introduction; 2 Related Work; 3 Approach; 3.1 Query Representation; 3.2 User Representation; 4 Model Initialization; 4.1 Embedding Initialization; 4.2 Topic Initialization; 5 Model Learning; 5.1 Addressing Label Noise; 5.2 Multi-task Learning; 6 Experiment Setup; 6.1 Dataset; 6.2 Compared Methods; 6.3 Implementation Details; 6.4 Evaluation Metric; 6.5 Results and Discussion; 7 Conclusions; References; A User Effort Measurement for Query Selection; 1 Introduction; 2 Related Works
3 User Effort Measurement4 Statistical Results; 5 Descriptive Examples; 6 Simulation Results; 7 Conclusion; 8 Discussion; References; Temporal Smoothing: Discriminatively Incorporating Various Temporal Profiles of Queries; 1 Introduction; 2 Related Work; 2.1 Document-Dependent Smoothing Methods; 2.2 Time-Sensitive Smoothing Methods; 3 Temporal Smoothing; 3.1 Temporal Profile; 3.2 Temporal Query Model; 3.3 Temporal Smoothing by Using Background Temporal Model; 3.4 An Unified Solution; 4 Experiment and Evaluation; 4.1 Corpus; 4.2 Queries; 4.3 Temporal Smoothing Result; 4.4 Retrieval Result
4.5 Analysing and Evaluating5 Conclusions and Future Work; References; Investigating Query Reformulation Behavior of Search Users; 1 Introduction; 2 Related Work; 3 Dataset; 4 Session-Level User Reformulation Behavior Analysis; 4.1 Analysis of Session-Level User Reformulation Pattern; 4.2 User Reformulation Behavior; 4.3 Conclusions; 5 Discussions and Future Work; References; LTRRS: A Learning to Rank Based Algorithm for Resource Selection in Distributed Information Retrieval; Abstract; 1 Introduction; 2 Related Works; 2.1 Large-Document Methods; 2.2 Small-Document Methods
2.3 Supervised Methods3 Framework; 3.1 Definitions; 3.2 Architecture; 3.3 Preprocess Module; 3.4 Resource Description Module; 3.5 Learning Module; 4 Multi-scale Features; 4.1 Term Matching Features; 4.2 CSI-Based Features; 4.3 Topical Relevance Features; 5 The Proposed Algorithm; 6 Experiments; 6.1 Dataset; 6.2 CSI Setup; 6.3 Result Analysis; 6.3.1 Performance Comparison; 6.3.2 Feature Analysis; 7 Conclusion; Acknowledgement; References; Knowledge and Entities; Simplified Representation Learning Model Based on Parameter-Sharing for Knowledge Graph Completion; 1 Introduction
2 Notation and Definition3 Related Work; 4 Simplified Representation Learning Model Based on Parameter-Sharing for Knowledge Graph Completion; 4.1 Overview; 4.2 Optimization; 5 Experiments; 5.1 Experiment Settings; 5.2 Link Prediction; 5.3 Triple Classification; 6 Conclusions; References; Document-Level Named Entity Recognition by Incorporating Global and Neighbor Features; 1 Introduction; 2 Related Work; 2.1 Sentence-Level NER; 2.2 Document-Level NER; 3 Our Document-Level NER Model: GNG; 3.1 Sentence-Level Bi-directional LSTM; 3.2 Document-Level Module; 3.3 CRF Layer; 4 Experiments
3 User Effort Measurement4 Statistical Results; 5 Descriptive Examples; 6 Simulation Results; 7 Conclusion; 8 Discussion; References; Temporal Smoothing: Discriminatively Incorporating Various Temporal Profiles of Queries; 1 Introduction; 2 Related Work; 2.1 Document-Dependent Smoothing Methods; 2.2 Time-Sensitive Smoothing Methods; 3 Temporal Smoothing; 3.1 Temporal Profile; 3.2 Temporal Query Model; 3.3 Temporal Smoothing by Using Background Temporal Model; 3.4 An Unified Solution; 4 Experiment and Evaluation; 4.1 Corpus; 4.2 Queries; 4.3 Temporal Smoothing Result; 4.4 Retrieval Result
4.5 Analysing and Evaluating5 Conclusions and Future Work; References; Investigating Query Reformulation Behavior of Search Users; 1 Introduction; 2 Related Work; 3 Dataset; 4 Session-Level User Reformulation Behavior Analysis; 4.1 Analysis of Session-Level User Reformulation Pattern; 4.2 User Reformulation Behavior; 4.3 Conclusions; 5 Discussions and Future Work; References; LTRRS: A Learning to Rank Based Algorithm for Resource Selection in Distributed Information Retrieval; Abstract; 1 Introduction; 2 Related Works; 2.1 Large-Document Methods; 2.2 Small-Document Methods
2.3 Supervised Methods3 Framework; 3.1 Definitions; 3.2 Architecture; 3.3 Preprocess Module; 3.4 Resource Description Module; 3.5 Learning Module; 4 Multi-scale Features; 4.1 Term Matching Features; 4.2 CSI-Based Features; 4.3 Topical Relevance Features; 5 The Proposed Algorithm; 6 Experiments; 6.1 Dataset; 6.2 CSI Setup; 6.3 Result Analysis; 6.3.1 Performance Comparison; 6.3.2 Feature Analysis; 7 Conclusion; Acknowledgement; References; Knowledge and Entities; Simplified Representation Learning Model Based on Parameter-Sharing for Knowledge Graph Completion; 1 Introduction
2 Notation and Definition3 Related Work; 4 Simplified Representation Learning Model Based on Parameter-Sharing for Knowledge Graph Completion; 4.1 Overview; 4.2 Optimization; 5 Experiments; 5.1 Experiment Settings; 5.2 Link Prediction; 5.3 Triple Classification; 6 Conclusions; References; Document-Level Named Entity Recognition by Incorporating Global and Neighbor Features; 1 Introduction; 2 Related Work; 2.1 Sentence-Level NER; 2.2 Document-Level NER; 3 Our Document-Level NER Model: GNG; 3.1 Sentence-Level Bi-directional LSTM; 3.2 Document-Level Module; 3.3 CRF Layer; 4 Experiments