M
Marie-Francine Moens
Researcher at Katholieke Universiteit Leuven
Publications - 410
Citations - 8987
Marie-Francine Moens is an academic researcher from Katholieke Universiteit Leuven. The author has contributed to research in topics: Information extraction & Language model. The author has an hindex of 45, co-authored 393 publications receiving 7779 citations. Previous affiliations of Marie-Francine Moens include Brandeis University & University of Copenhagen Faculty of Science.
Papers
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Journal ArticleDOI
A Survey on Temporal Reasoning for Temporal Information Extraction from Text
TL;DR: This article presents a comprehensive survey of the research from the past decades on temporal reasoning for automatic temporal information extraction from text, providing a case study on the integration of symbolic reasoning with machine learning-based information extraction systems.
Proceedings Article
Study on Sentence Relations in the Automatic Detection of Argumentation in Legal Cases
TL;DR: The results of experiments prove that the analysis of relations between sentences increase the accuracy in the automatic detection of arguments in legal cases and treats the search of arguments as a classification problem.
Journal ArticleDOI
Transfer Learning for Named Entity Recognition in Financial and Biomedical Documents
TL;DR: It is demonstrated through experiments that NER models trained on labeled data from a source domain can be used as base models and then be fine-tuned with few labeled data for recognition of different named entity classes in a target domain.
Proceedings ArticleDOI
Combining structured and unstructured information in a retrieval model for accessing legislation
TL;DR: In this paper, the authors report on several XML retrieval models that are explicitly designed for the retrieval of legislation and show that the models provide more advanced access to the content of statutes.
Journal ArticleDOI
Fast and Flexible Top-k Similarity Search on Large Networks
Jing Zhang,Jie Tang,Cong Ma,Hanghang Tong,Yu Jing,Juanzi Li,Walter Luyten,Marie-Francine Moens +7 more
TL;DR: A sampling-based method using random paths to estimate the similarities based on both common neighbors and structural contexts efficiently in very large homogeneous or heterogeneous information networks.