Advances in information retrieval [electronic resource] : 42nd European Conference on IR Research, ECIR 2020, Lisbon, Portugal, April 14-17, 2020, Proceedings. Part I / Joemon M. Jose, Emine Yilmaz, João Magalhães, Pablo Castells, Nicola Ferro, Mário J. Silva, Flávio Martins (eds.).

By: (42nd : European Conference on IR Research (42nd : 2020 : Lisbon, Portugal)
Contributor(s): Jose, Joemon M | Yilmaz, Emine (Computer engineer) | Magalhães, João | Castells, Pablo | Ferro, Nicola | Silva, Mário J | Martins, Flávio
Material type: TextTextSeries: SerienbezeichnungLecture notes in computer science: 12035.; LNCS sublibrary: Publisher: Cham : Springer, 2020Description: 1 online resource (896 p.)ISBN: 9783030454395; 3030454398Other title: ECIR 2020Subject(s): Information retrieval -- Congresses | Data mining -- Congresses | Information storage and retrieval systems -- CongressesGenre/Form: Electronic books. | Electronic books. Additional physical formats: Print version:: Advances in Information Retrieval : 42nd European Conference on IR Research, ECIR 2020, Lisbon, Portugal, April 14-17, 2020, Proceedings, Part IDDC classification: 025.0425 LOC classification: Z667Online resources: Click here to access online
Contents:
Intro -- Preface -- Organization -- Abstracts of Keynotes -- Task-Based Intelligent Retrieval and Recommendation -- Better Representations for Search Tasks -- Focusing the Macroscope: How We Can Use Data to Understand Behavior -- Contents -- Part I -- Contents -- Part II -- Deep Learning I -- Seed-Guided Deep Document Clustering -- 1 Introduction -- 2 Related Work -- 3 Seed-Guided Deep Document Clustering -- 3.1 Training -- 4 Experiments -- 4.1 Results -- 5 Conclusion -- References -- Improving Knowledge Graph Embedding Using Locally and Globally Attentive Relation Paths -- 1 Introduction
2 Preliminaries -- 2.1 Problem Definition -- 2.2 ConvKB -- 3 Our Proposed Model -- 3.1 PConvKB -- 3.2 Measuring Local Importances of Relation Paths by Attention Mechanism -- 3.3 Measuring Global Importances of Relation Paths by Degree-Guided Inverse Path Frequency -- 3.4 Aggregating Feature Maps Using Pooling Operation -- 3.5 Model Training -- 3.6 Complexity Analysis -- 4 Experiments -- 4.1 Datasets -- 4.2 Comparison Methods -- 4.3 Link Prediction -- 4.4 Triple Classification -- 5 Related Work -- 6 Conclusion -- References
ReadNet: A Hierarchical Transformer Framework for Web Article Readability Analysis -- 1 Introduction -- 2 Related Work -- 3 Preliminary -- 3.1 Problem Definition -- 3.2 Explicit Features -- 4 Hierarchical Transformer for Readability Analysis -- 4.1 From Words to Sentences -- 4.2 From Sentences to Articles -- 4.3 Transfer Layer -- 4.4 Learning Objective -- 4.5 Why Hierarchical Self-attention -- 5 Experiments -- 5.1 Datasets -- 5.2 Evaluation -- 5.3 Analysis on Transfer Learning -- 6 Conclusion and Future Work -- References
Variational Recurrent Sequence-to-Sequence Retrieval for Stepwise Illustration -- 1 Introduction -- 2 Related Work -- 3 Stepwise Recipe Dataset Construction -- 4 Variational Recurrent Seq2seq (VRSS) Retrieval Model -- 5 Experimental Setup -- 5.1 Models for Comparison -- 5.2 Evaluation Methods -- 6 Results and Discussion -- 6.1 Automatic Evaluation -- 6.2 Human Evaluation -- 7 Conclusion -- References -- A Hierarchical Model for Data-to-Text Generation -- 1 Introduction -- 2 Related Work -- 3 Hierarchical Encoder Model for Data-to-Text -- 3.1 Notation and General Overview
3.2 Hierarchical Encoding Model -- 3.3 Hierarchical Attention -- 4 Experimental Setup -- 4.1 The Rotowire Dataset -- 4.2 Evaluation Metrics -- 4.3 Baselines -- 4.4 Implementation Details -- 5 Results -- 6 Conclusion and Future Work -- References -- Entities -- Context-Guided Learning to Rank Entities -- 1 Introduction -- 2 Related Work -- 2.1 Entity Ranking -- 2.2 Multi-task Learning -- 3 Methodology -- 3.1 Problem Definition -- 3.2 Context-Guided Learning -- 3.3 Context-Guided Learning for Ranking -- 3.4 Context Models -- 4 Experiments -- 4.1 Data -- 4.2 Experimental Settings
Summary: This two-volume set LNCS 12035 and 12036 constitutes the refereed proceedings of the 42nd European Conference on IR Research, ECIR 2020, held in Lisbon, Portugal, in April 2020. The 55 full papers presented together with 8 reproducibility papers, 46 short papers, 10 demonstration papers, 12 invited CLEF papers, 7 doctoral consortium papers, 4 workshop papers, and 3 tutorials were carefully reviewed and selected from 457 submissions. They were organized in topical sections named: Part I: deep learning I; entities; evaluation; recommendation; information extraction; deep learning II; retrieval; multimedia; deep learning III; queries; IR - general; question answering, prediction, and bias; and deep learning IV. Part II: reproducibility papers; short papers; demonstration papers; CLEF organizers lab track; doctoral consortium papers; workshops; and tutorials.
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International conference proceeding.

Description based upon print version of record.

Intro -- Preface -- Organization -- Abstracts of Keynotes -- Task-Based Intelligent Retrieval and Recommendation -- Better Representations for Search Tasks -- Focusing the Macroscope: How We Can Use Data to Understand Behavior -- Contents -- Part I -- Contents -- Part II -- Deep Learning I -- Seed-Guided Deep Document Clustering -- 1 Introduction -- 2 Related Work -- 3 Seed-Guided Deep Document Clustering -- 3.1 Training -- 4 Experiments -- 4.1 Results -- 5 Conclusion -- References -- Improving Knowledge Graph Embedding Using Locally and Globally Attentive Relation Paths -- 1 Introduction

2 Preliminaries -- 2.1 Problem Definition -- 2.2 ConvKB -- 3 Our Proposed Model -- 3.1 PConvKB -- 3.2 Measuring Local Importances of Relation Paths by Attention Mechanism -- 3.3 Measuring Global Importances of Relation Paths by Degree-Guided Inverse Path Frequency -- 3.4 Aggregating Feature Maps Using Pooling Operation -- 3.5 Model Training -- 3.6 Complexity Analysis -- 4 Experiments -- 4.1 Datasets -- 4.2 Comparison Methods -- 4.3 Link Prediction -- 4.4 Triple Classification -- 5 Related Work -- 6 Conclusion -- References

ReadNet: A Hierarchical Transformer Framework for Web Article Readability Analysis -- 1 Introduction -- 2 Related Work -- 3 Preliminary -- 3.1 Problem Definition -- 3.2 Explicit Features -- 4 Hierarchical Transformer for Readability Analysis -- 4.1 From Words to Sentences -- 4.2 From Sentences to Articles -- 4.3 Transfer Layer -- 4.4 Learning Objective -- 4.5 Why Hierarchical Self-attention -- 5 Experiments -- 5.1 Datasets -- 5.2 Evaluation -- 5.3 Analysis on Transfer Learning -- 6 Conclusion and Future Work -- References

Variational Recurrent Sequence-to-Sequence Retrieval for Stepwise Illustration -- 1 Introduction -- 2 Related Work -- 3 Stepwise Recipe Dataset Construction -- 4 Variational Recurrent Seq2seq (VRSS) Retrieval Model -- 5 Experimental Setup -- 5.1 Models for Comparison -- 5.2 Evaluation Methods -- 6 Results and Discussion -- 6.1 Automatic Evaluation -- 6.2 Human Evaluation -- 7 Conclusion -- References -- A Hierarchical Model for Data-to-Text Generation -- 1 Introduction -- 2 Related Work -- 3 Hierarchical Encoder Model for Data-to-Text -- 3.1 Notation and General Overview

3.2 Hierarchical Encoding Model -- 3.3 Hierarchical Attention -- 4 Experimental Setup -- 4.1 The Rotowire Dataset -- 4.2 Evaluation Metrics -- 4.3 Baselines -- 4.4 Implementation Details -- 5 Results -- 6 Conclusion and Future Work -- References -- Entities -- Context-Guided Learning to Rank Entities -- 1 Introduction -- 2 Related Work -- 2.1 Entity Ranking -- 2.2 Multi-task Learning -- 3 Methodology -- 3.1 Problem Definition -- 3.2 Context-Guided Learning -- 3.3 Context-Guided Learning for Ranking -- 3.4 Context Models -- 4 Experiments -- 4.1 Data -- 4.2 Experimental Settings

4.3 Experimental Results

Includes author index.

This two-volume set LNCS 12035 and 12036 constitutes the refereed proceedings of the 42nd European Conference on IR Research, ECIR 2020, held in Lisbon, Portugal, in April 2020. The 55 full papers presented together with 8 reproducibility papers, 46 short papers, 10 demonstration papers, 12 invited CLEF papers, 7 doctoral consortium papers, 4 workshop papers, and 3 tutorials were carefully reviewed and selected from 457 submissions. They were organized in topical sections named: Part I: deep learning I; entities; evaluation; recommendation; information extraction; deep learning II; retrieval; multimedia; deep learning III; queries; IR - general; question answering, prediction, and bias; and deep learning IV. Part II: reproducibility papers; short papers; demonstration papers; CLEF organizers lab track; doctoral consortium papers; workshops; and tutorials.

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