21 citations · 35 across the 19 of their papers we have counts for
19 papers
TimeRoute: Time-Aware Modality Routing and Diffusion for Multi-Modal Recommendation
Pengyu Zhang, Yangqin Jiang, Klim Zaporojets +2
Multi-modal recommenders fuse user-item interaction signals with item modalities such as text, images, and audio, but the usefulness of each drifts over time and at different rates…
Select, Don't Train: The Benefits of Modular Entity Disambiguation with LLM-Based Selection
Fina Polat, Daniel Daza, Pengyu Zhang +2
Entity Disambiguation (ED) is a key task for constructing and using knowledge graphs. State-of-the-art neural approaches commonly model ED as a single task, although it consists of…
Time Imprint: Learning Time-Aware Representations in Multi-Modal Knowledge Graphs
Pengyu Zhang, Klim Zaporojets, Congfeng Cao +2
Multi-Modal Knowledge Graphs (MMKGs) enrich entities with multiple modalities such as text and images, yet entities with highly similar multi-modal features remain difficult to dis…
Are a Thousand Words Better Than a Single Picture? Beyond Images -- A Framework for Multi-Modal Knowledge Graph Dataset Enrichment
Pengyu Zhang, Klim Zaporojets, Jie Liu +2
Multi-Modal Knowledge Graphs (MMKGs) benefit from visual information, yet large-scale image collection is hard to curate and often excludes ambiguous but relevant visuals (e.g., lo…
EMERGE: A Benchmark for Updating Knowledge Graphs with Emerging Textual Knowledge
Klim Zaporojets, Daniel Daza, Edoardo Barba +3
Knowledge Graphs (KGs) are structured knowledge repositories containing entities and relations between them. In this paper, we study the problem of automatically updating KGs over…
A Survey of Large Language Models for Data Challenges in Graphs
Mengran Li, Pengyu Zhang, Wenbin Xing +11
Graphs are a widely used paradigm for representing non-Euclidean data, with applications ranging from social network analysis to biomolecular prediction. While graph learning has a…
Influences on LLM Calibration: A Study of Response Agreement, Loss Functions, and Prompt Styles
Yuxi Xia, Pedro Henrique Luz de Araujo, Klim Zaporojets +1
Calibration, the alignment between model confidence and prediction accuracy, is critical for the reliable deployment of large language models (LLMs). Existing works neglect to meas…
CYCLE: Cross-Year Contrastive Learning in Entity-Linking
Pengyu Zhang, Congfeng Cao, Klim Zaporojets +1
Knowledge graphs constantly evolve with new entities emerging, existing definitions being revised, and entity relationships changing. These changes lead to temporal degradation in…
Analysing zero-shot temporal relation extraction on clinical notes using temporal consistency
Vasiliki Kougia, Anastasiia Sedova, Andreas Stephan +2
This paper presents the first study for temporal relation extraction in a zero-shot setting focusing on biomedical text. We employ two types of prompts and five LLMs (GPT-3.5, Mixt…
Wiki Entity Summarization Benchmark
Saeedeh Javadi, Atefeh Moradan, Mohammad Sorkhpar +3
Entity summarization aims to compute concise summaries for entities in knowledge graphs. Existing datasets and benchmarks are often limited to a few hundred entities and discard gr…
Personality Style Recognition via Machine Learning: Identifying Anaclitic and Introjective Personality Styles from Patients' Speech
Semere Kiros Bitew, Vincent Schelstraete, Klim Zaporojets +3
In disentangling the heterogeneity observed in psychopathology, personality of the patients is considered crucial. While it has been demonstrated that personality traits are reflec…
BioDEX: Large-Scale Biomedical Adverse Drug Event Extraction for Real-World Pharmacovigilance
Karel D'Oosterlinck, François Remy, Johannes Deleu +7
Timely and accurate extraction of Adverse Drug Events (ADE) from biomedical literature is paramount for public safety, but involves slow and costly manual labor. We set out to impr…
Neural Approaches to Entity-Centric Information Extraction
Klim Zaporojets
Artificial Intelligence (AI) has huge impact on our daily lives with applications such as voice assistants, facial recognition, chatbots, autonomously driving cars, etc. Natural La…
TempEL: Linking Dynamically Evolving and Newly Emerging Entities
Klim Zaporojets, Lucie-Aimee Kaffee, Johannes Deleu +3
In our continuously evolving world, entities change over time and new, previously non-existing or unknown, entities appear. We study how this evolutionary scenario impacts the perf…
CookDial: A dataset for task-oriented dialogs grounded in procedural documents
Yiwei Jiang, Klim Zaporojets, Johannes Deleu +2
This work presents a new dialog dataset, CookDial, that facilitates research on task-oriented dialog systems with procedural knowledge understanding. The corpus contains 260 human-…
Towards Consistent Document-level Entity Linking: Joint Models for Entity Linking and Coreference Resolution
Klim Zaporojets, Johannes Deleu, Yiwei Jiang +2
We consider the task of document-level entity linking (EL), where it is important to make consistent decisions for entity mentions over the full document jointly. We aim to leverag…
Injecting Knowledge Base Information into End-to-End Joint Entity and Relation Extraction and Coreference Resolution
Severine Verlinden, Klim Zaporojets, Johannes Deleu +2
We consider a joint information extraction (IE) model, solving named entity recognition, coreference resolution and relation extraction jointly over the whole document. In particul…
DWIE: an entity-centric dataset for multi-task document-level information extraction
Klim Zaporojets, Johannes Deleu, Chris Develder +1
This paper presents DWIE, the 'Deutsche Welle corpus for Information Extraction', a newly created multi-task dataset that combines four main Information Extraction (IE) annotation…
Solving Arithmetic Word Problems by Scoring Equations with Recursive Neural Networks
Klim Zaporojets, Giannis Bekoulis, Johannes Deleu +2
Solving arithmetic word problems is a cornerstone task in assessing language understanding and reasoning capabilities in NLP systems. Recent works use automatic extraction and rank…