Table of Contents

Deep Learning I.- Seed-guided Deep Document Clustering.- Improving Knowledge Graph Embedding using Locally and Globally Attentive Relation Paths.- ReadNet: A Hierarchical Transformer Framework for Web Article Readability Analysis.- Variational Recurrent Sequence-to-Sequence Retrieval for Stepwise Illustration.- A Hierarchical Model for Data-to-Text Generation.- Entities.- Context-guided Learning to Rank Entities.- Graph-Embedding Empowered Entity Retrieval.- Learning Advanced Similarities and Training Features for Toponym Interlinking.- Patch-based Identification of Lexical Semantic Relations.- Joint Word and Entity Embeddings for Entity Retrieval from a Knowledge Graph.- Evaluation.- Evaluating the Effectiveness of the Standard Insights Extraction Pipeline for Bantu Languages.- Recommendation.- Axiomatic Analysis of Contact Recommendation Methods in Social Networks: an IR perspective.- Recommending Music Curators: A Neural Style-Aware Approach.- Joint Geographical and Temporal Modeling based on Matrix Factorization for Point-of-Interest Recommendation.- Semantic Modelling of Citation Contexts for Context-aware Citation Recommendation.- TransRev: Modeling Reviews as Translations from Users to Items.- Information Extraction.- Domain-independent Extraction of Scientific Concepts from Research Articles.- Leveraging Schema Labels to Enhance Dataset Search.- Moving from formal towards coherent concept analysis: why, when and how.- Beyond Modelling: Understanding Mental Disorders in Online Social Media.- Deep Learning II.- Learning based Methods for Code Runtime Complexity Prediction.- Inductive Document Network Embedding with Topic-Word Attention.- Multi-components system for automatic Arabic diacritization.- A Mixed Semantic Features Model for Chinese NER with Characters and Words.- VGCN-BERT: Augmenting BERT with Graph Embedding for Text Classification.- Retrieval.- A Computational Approach for Objectively Derived Systematic Review Search Strategies.- You Can Teach an Old Dog New Tricks: Rank Fusion applied to Coordination Level Matching for Ranking in Systematic Reviews.- Counterfactual Online Learning to Rank.- A Framework for Argument Retrieval: Ranking Argument Clusters by Frequency and Specificity.- Relevance Ranking based on Query-Aware Context Analysis.- Multimedia.- Multimodal Entity Linking for Tweets.- MEMIS: Multimodal Emergency Management Information System.- Interactive Learning for Multimedia at Large.- Visual Re-ranking via Adaptive Collaborative Hypergraph Learning for Image Retrieval.- Motion Words: A Text-like Representation of 3D Skeleton Sequences.- Deep Learning III.- Reinforced Rewards Framework for Text Style Transfer.- Recognizing Semantic Relations: Attention-Based Transformers vs. Recurrent Models.- Early detection of rumours on Twitter via stance transfer learning.- Learning to Rank Images with Cross-Modal Graph Convolutions.- Diagnosing BERT with RetrievalHeuristics.- Queries.- Generation of Synthetic Query Auto Completion Logs.- What Can Task Teach Us About Query Reformulations?.- A Regularised Intent Model for Discovering Multiple Intents in E-Commerce Tail Queries.- Utilising Information Foraging Theory for User Interaction with Image Query Auto-Completion.- Using Image Captions and Multitask Learning for Recommending Query Reformulations.- IR – general.- Curriculum Learning Strategies for IR: An Empirical Study on Conversation Response Ranking.- Accelerating Substructure Similarity Search for Formula Retrieval.- Quantum-like Structure in Multidimensional Relevance Judgements.- Question Answering, Prediction, and Bias.- Temporal Latent Space Modeling for Community Prediction.- KvGR: A Graph-Based Interface for Explorative Sequential Question Answering on Heterogeneous Information Sources.- Answering Event-Related Questions over Long-term News Article Archives.- bias goggles - Graph-based Computation of the Bias of Web Domains through the Eyes of Users.- Deep Learning IV.- Biconditional Generative Adversarial Networks for Multiview Learning with Missing Views.- Semantic Path-Based Learning for Review Volume Prediction.- An Attention Model of Customer Expectation to Improve Review Helpfulness Prediction.