Ágnes Sándor

Also published as: Agnes Sandor


2021

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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.

2012

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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

2009

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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)