Ágnes Sándor

Also published as: Agnes Sandor


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

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.


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Identifying Claimed Knowledge Updates in Biomedical Research Articles
Ágnes Sándor | Anita de Waard
Proceedings of the Workshop on Detecting Structure in Scholarly Discourse

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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


Detecting key sentences for automatic assistance in peer reviewing research articles in educational sciences
Ágnes Sándor | Angela Vorndran
Proceedings of the 2009 Workshop on Text and Citation Analysis for Scholarly Digital Libraries (NLPIR4DL)