@inproceedings{rasmussen-etal-2019-cross,
title = "Cross-Domain Sentiment Classification using Vector Embedded Domain Representations",
author = "Rasmussen, Nicolaj Filrup and
Jensen, Kristian N{\o}rgaard and
Placenti, Marco and
Wang, Thai",
editor = {Nivre, Joakim and
Derczynski, Leon and
Ginter, Filip and
Lindi, Bj{\o}rn and
Oepen, Stephan and
S{\o}gaard, Anders and
Tidemann, J{\"o}rg},
booktitle = "Proceedings of the First NLPL Workshop on Deep Learning for Natural Language Processing",
month = sep,
year = "2019",
address = "Turku, Finland",
publisher = {Link{\"o}ping University Electronic Press},
url = "https://aclanthology.org/W19-6206",
pages = "48--57",
abstract = "Due to the differences between reviews in different product categories, creating a general model for cross-domain sentiment classification can be a difficult task. This paper proposes an architecture that incorporates domain knowledge into a neural sentiment classification model. In addition to providing a cross-domain model, this also provides a quantifiable representation of the domains as numeric vectors. We show that it is possible to cluster the domain vectors and provide qualitative insights into the inter-domain relations. We also a) present a new data set for sentiment classification that includes a domain parameter and preprocessed data points, and b) perform an ablation study in order to determine whether some word groups impact performance.",
}
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%0 Conference Proceedings
%T Cross-Domain Sentiment Classification using Vector Embedded Domain Representations
%A Rasmussen, Nicolaj Filrup
%A Jensen, Kristian Nørgaard
%A Placenti, Marco
%A Wang, Thai
%Y Nivre, Joakim
%Y Derczynski, Leon
%Y Ginter, Filip
%Y Lindi, Bjørn
%Y Oepen, Stephan
%Y Søgaard, Anders
%Y Tidemann, Jörg
%S Proceedings of the First NLPL Workshop on Deep Learning for Natural Language Processing
%D 2019
%8 September
%I Linköping University Electronic Press
%C Turku, Finland
%F rasmussen-etal-2019-cross
%X Due to the differences between reviews in different product categories, creating a general model for cross-domain sentiment classification can be a difficult task. This paper proposes an architecture that incorporates domain knowledge into a neural sentiment classification model. In addition to providing a cross-domain model, this also provides a quantifiable representation of the domains as numeric vectors. We show that it is possible to cluster the domain vectors and provide qualitative insights into the inter-domain relations. We also a) present a new data set for sentiment classification that includes a domain parameter and preprocessed data points, and b) perform an ablation study in order to determine whether some word groups impact performance.
%U https://aclanthology.org/W19-6206
%P 48-57
Markdown (Informal)
[Cross-Domain Sentiment Classification using Vector Embedded Domain Representations](https://aclanthology.org/W19-6206) (Rasmussen et al., NoDaLiDa 2019)
ACL