@inproceedings{guhr-etal-2020-training,
title = "Training a Broad-Coverage {G}erman Sentiment Classification Model for Dialog Systems",
author = {Guhr, Oliver and
Schumann, Anne-Kathrin and
Bahrmann, Frank and
B{\"o}hme, Hans Joachim},
booktitle = "Proceedings of the 12th Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.202",
pages = "1627--1632",
abstract = "This paper describes the training of a general-purpose German sentiment classification model. Sentiment classification is an important aspect of general text analytics. Furthermore, it plays a vital role in dialogue systems and voice interfaces that depend on the ability of the system to pick up and understand emotional signals from user utterances. The presented study outlines how we have collected a new German sentiment corpus and then combined this corpus with existing resources to train a broad-coverage German sentiment model. The resulting data set contains 5.4 million labelled samples. We have used the data to train both, a simple convolutional and a transformer-based classification model and compared the results achieved on various training configurations. The model and the data set will be published along with this paper.",
language = "English",
ISBN = "979-10-95546-34-4",
}
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%0 Conference Proceedings
%T Training a Broad-Coverage German Sentiment Classification Model for Dialog Systems
%A Guhr, Oliver
%A Schumann, Anne-Kathrin
%A Bahrmann, Frank
%A Böhme, Hans Joachim
%S Proceedings of the 12th Language Resources and Evaluation Conference
%D 2020
%8 may
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G English
%F guhr-etal-2020-training
%X This paper describes the training of a general-purpose German sentiment classification model. Sentiment classification is an important aspect of general text analytics. Furthermore, it plays a vital role in dialogue systems and voice interfaces that depend on the ability of the system to pick up and understand emotional signals from user utterances. The presented study outlines how we have collected a new German sentiment corpus and then combined this corpus with existing resources to train a broad-coverage German sentiment model. The resulting data set contains 5.4 million labelled samples. We have used the data to train both, a simple convolutional and a transformer-based classification model and compared the results achieved on various training configurations. The model and the data set will be published along with this paper.
%U https://aclanthology.org/2020.lrec-1.202
%P 1627-1632
Markdown (Informal)
[Training a Broad-Coverage German Sentiment Classification Model for Dialog Systems](https://aclanthology.org/2020.lrec-1.202) (Guhr et al., LREC 2020)
ACL