Papers by Gijs van Dijck
Finding the Law: Enhancing Statutory Article Retrieval via Graph Neural Networks (2023.eacl-main)
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| Challenge: | Statutory article retrieval (SAR) is a promising application of legal text processing. |
| Approach: | They propose a graph-augmented dense statute retriever model that incorporates the structure of legislation via a neural network to improve density retrieval performance. |
| Outcome: | The proposed model outperforms baselines on a real-world expert-annotated dataset. |
Know When to Fuse: Investigating Non-English Hybrid Retrieval in the Legal Domain (2025.coling-main)
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| Challenge: | Existing research focuses on a limited set of retrieval methods, evaluated in pairs on domain-general datasets exclusively in English. |
| Approach: | They evaluate the efficacy of hybrid search across a variety of retrieval models in the french language . they find that fusion of different domain-general models consistently enhances performance . |
| Outcome: | The proposed model improves in-domain performance compared to a single model in a zero-shot context . the proposed model also improves when the models are trained in- domain . |
IDTraffickers: An Authorship Attribution Dataset to link and connect Potential Human-Trafficking Operations on Text Escort Advertisements (2023.emnlp-main)
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| Challenge: | a significant number of human trafficking cases are associated with online advertisements . identification of HT vendors is challenging for law enforcement agencies . |
| Approach: | IDTraffickers uses 87,595 text ads and 5,244 vendor labels to link HT vendors . a macro-F1 score is achieved in a closed-set classification environment . |
| Outcome: | IDTraffickers is a dataset that enables verification and identification of HT vendors . the model achieves a macro-F1 score in a closed-set classification environment . |
VendorLink: An NLP approach for Identifying & Linking Vendor Migrants & Potential Aliases on Darknet Markets (2023.acl-long)
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| Challenge: | Anonymity on the Darknet allows vendors to stay undetected by using multiple vendor aliases or frequently migrating between markets. |
| Approach: | They propose an NLP-based approach that examines writing patterns to verify, identify, and link unique vendor accounts across text advertisements on seven public Darknet markets. |
| Outcome: | The proposed approach can help law enforcement agencies make more informed decisions by verifying and identifying migrating vendors and their potential aliases on existing and Low-Resource (LR) emerging Darknet markets. |
ColBERT-XM: A Modular Multi-Vector Representation Model for Zero-Shot Multilingual Information Retrieval (2025.coling-main)
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| Challenge: | Existing approaches to improve retrieval effectiveness in high-resource languages are limited due to the lack of high-quality labeled data in non-English languages. |
| Approach: | They propose a modular dense retrieval model that learns from the rich data of a single high-resource language and effectively zero-shot transfers to a wide array of languages. |
| Outcome: | The proposed model performs well against state-of-the-art multilingual retrieval models trained on more extensive datasets in various languages. |