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        <title>Modelling Representation Noise in Emotion Analysis using Gaussian Processes</title>
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            <title>Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)</title>
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            <namePart type="given">Greg</namePart>
            <namePart type="family">Kondrak</namePart>
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        <name type="personal">
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            <namePart type="family">Watanabe</namePart>
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    <abstract>Emotion Analysis is the task of modelling latent emotions present in natural language. Labelled datasets for this task are scarce so learning good input text representations is not trivial. Using averaged word embeddings is a simple way to leverage unlabelled corpora to build text representations but this approach can be prone to noise either coming from the embedding themselves or the averaging procedure. In this paper we propose a model for Emotion Analysis using Gaussian Processes and kernels that are better suitable for functions that exhibit noisy behaviour. Empirical evaluations in a emotion prediction task show that our model outperforms commonly used baselines for regression.</abstract>
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%0 Conference Proceedings
%T Modelling Representation Noise in Emotion Analysis using Gaussian Processes
%A Beck, Daniel
%Y Kondrak, Greg
%Y Watanabe, Taro
%S Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
%D 2017
%8 November
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F beck-2017-modelling
%X Emotion Analysis is the task of modelling latent emotions present in natural language. Labelled datasets for this task are scarce so learning good input text representations is not trivial. Using averaged word embeddings is a simple way to leverage unlabelled corpora to build text representations but this approach can be prone to noise either coming from the embedding themselves or the averaging procedure. In this paper we propose a model for Emotion Analysis using Gaussian Processes and kernels that are better suitable for functions that exhibit noisy behaviour. Empirical evaluations in a emotion prediction task show that our model outperforms commonly used baselines for regression.
%U https://aclanthology.org/I17-2024
%P 140-145

Markdown (Informal)

[Modelling Representation Noise in Emotion Analysis using Gaussian Processes](https://aclanthology.org/I17-2024) (Beck, IJCNLP 2017)

• Modelling Representation Noise in Emotion Analysis using Gaussian Processes (Beck, IJCNLP 2017)

ACL

• Daniel Beck. 2017. Modelling Representation Noise in Emotion Analysis using Gaussian Processes. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers), pages 140–145, Taipei, Taiwan. Asian Federation of Natural Language Processing.