Ágnes Sándor
Also published as: Agnes Sandor
2021
Semantic Context Path Labeling for Semantic Exploration of User Reviews
Salah Aït-Mokhtar | Caroline Brun | Yves Hoppenot | Agnes Sandor
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
Salah Aït-Mokhtar | Caroline Brun | Yves Hoppenot | Agnes Sandor
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
In this paper we present a prototype demonstrator showcasing a novel method to perform semantic exploration of user reviews. The system enables effective navigation in a rich contextual semantic schema with a large number of structured classes indicating relevant information. In order to identify instances of the structured classes in the reviews, we defined a new Information Extraction task called Semantic Context Path (SCP) labeling, which simultaneously assigns types and semantic roles to entity mentions. Reviews can rapidly be explored based on the fine-grained and structured semantic classes. As a proof-of-concept, we have implemented this system for reviews on Points-of-Interest, in English and Korean.
2012
Identifying Claimed Knowledge Updates in Biomedical Research Articles
Ágnes Sándor | Anita de Waard
Proceedings of the Workshop on Detecting Structure in Scholarly Discourse
Ágnes Sándor | Anita de Waard
Proceedings of the Workshop on Detecting Structure in Scholarly Discourse
Hybrid Adaptation of Named Entity Recognition for Statistical Machine Translation
Vassilina Nikoulina | Agnes Sandor | Marc Dymetman
Proceedings of the Second Workshop on Applying Machine Learning Techniques to Optimise the Division of Labour in Hybrid MT
Vassilina Nikoulina | Agnes Sandor | Marc Dymetman
Proceedings of the Second Workshop on Applying Machine Learning Techniques to Optimise the Division of Labour in Hybrid MT