Erik van der Goot

Also published as: Erik Van der Goot


2017

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Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
Alexandra Balahur | Saif M. Mohammad | Erik van der Goot
Proceedings of the 8th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

2016

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Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
Alexandra Balahur | Erik van der Goot | Piek Vossen | Andres Montoyo
Proceedings of the 7th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

2015

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Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
Alexandra Balahur | Erik van der Goot | Piek Vossen | Andres Montoyo
Proceedings of the 6th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

2014

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Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
Alexandra Balahur | Erik van der Goot | Ralf Steinberger | Andres Montoyo
Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

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Challenges in Creating a Multilingual Sentiment Analysis Application for Social Media Mining
Alexandra Balahur | Hristo Tanev | Erik van der Goot
Proceedings of the 5th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

2013

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Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis
Alexandra Balahur | Erik van der Goot | Andres Montoyo
Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

2012

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ONTS: “Optima” News Translation System
Marco Turchi | Martin Atkinson | Alastair Wilcox | Brett Crawley | Stefano Bucci | Ralf Steinberger | Erik Van der Goot
Proceedings of the Demonstrations at the 13th Conference of the European Chapter of the Association for Computational Linguistics

2011

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JRC-NAMES: A Freely Available, Highly Multilingual Named Entity Resource
Ralf Steinberger | Bruno Pouliquen | Mijail Kabadjov | Jenya Belyaeva | Erik van der Goot
Proceedings of the International Conference Recent Advances in Natural Language Processing 2011

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Highly Multilingual Coreference Resolution Exploiting a Mature Entity Repository
Josef Steinberger | Jenya Belyaeva | Jonathan Crawley | Leonida Della-Rocca | Mohamed Ebrahim | Maud Ehrmann | Mijail Kabadjov | Ralf Steinberger | Erik van der Goot
Proceedings of the International Conference Recent Advances in Natural Language Processing 2011

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Multilingual Entity-Centered Sentiment Analysis Evaluated by Parallel Corpora
Josef Steinberger | Polina Lenkova | Mijail Kabadjov | Ralf Steinberger | Erik van der Goot
Proceedings of the International Conference Recent Advances in Natural Language Processing 2011

2010

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Sentiment Analysis in the News
Alexandra Balahur | Ralf Steinberger | Mijail Kabadjov | Vanni Zavarella | Erik van der Goot | Matina Halkia | Bruno Pouliquen | Jenya Belyaeva
Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10)

Recent years have brought a significant growth in the volume of research in sentiment analysis, mostly on highly subjective text types (movie or product reviews). The main difference these texts have with news articles is that their target is clearly defined and unique across the text. Following different annotation efforts and the analysis of the issues encountered, we realised that news opinion mining is different from that of other text types. We identified three subtasks that need to be addressed: definition of the target; separation of the good and bad news content from the good and bad sentiment expressed on the target; and analysis of clearly marked opinion that is expressed explicitly, not needing interpretation or the use of world knowledge. Furthermore, we distinguish three different possible views on newspaper articles ― author, reader and text, which have to be addressed differently at the time of analysing sentiment. Given these definitions, we present work on mining opinions about entities in English language news, in which we apply these concepts. Results showed that this idea is more appropriate in the context of news opinion mining and that the approaches taking this into consideration produce a better performance.