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        <title>Predicting Human Activities from User-Generated Content</title>
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            <namePart type="given">Anna</namePart>
            <namePart type="family">Korhonen</namePart>
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    <abstract>The activities we do are linked to our interests, personality, political preferences, and decisions we make about the future. In this paper, we explore the task of predicting human activities from user-generated content. We collect a dataset containing instances of social media users writing about a range of everyday activities. We then use a state-of-the-art sentence embedding framework tailored to recognize the semantics of human activities and perform an automatic clustering of these activities. We train a neural network model to make predictions about which clusters contain activities that were performed by a given user based on the text of their previous posts and self-description. Additionally, we explore the degree to which incorporating inferred user traits into our model helps with this prediction task.</abstract>
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    <identifier type="doi">10.18653/v1/P19-1245</identifier>
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%0 Conference Proceedings
%T Predicting Human Activities from User-Generated Content
%A Wilson, Steven
%A Mihalcea, Rada
%Y Korhonen, Anna
%Y Traum, David
%Y Màrquez, Lluís
%S Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
%D 2019
%8 July
%I Association for Computational Linguistics
%C Florence, Italy
%F wilson-mihalcea-2019-predicting
%X The activities we do are linked to our interests, personality, political preferences, and decisions we make about the future. In this paper, we explore the task of predicting human activities from user-generated content. We collect a dataset containing instances of social media users writing about a range of everyday activities. We then use a state-of-the-art sentence embedding framework tailored to recognize the semantics of human activities and perform an automatic clustering of these activities. We train a neural network model to make predictions about which clusters contain activities that were performed by a given user based on the text of their previous posts and self-description. Additionally, we explore the degree to which incorporating inferred user traits into our model helps with this prediction task.
%R 10.18653/v1/P19-1245
%U https://aclanthology.org/P19-1245
%U https://doi.org/10.18653/v1/P19-1245
%P 2572-2582

Markdown (Informal)

[Predicting Human Activities from User-Generated Content](https://aclanthology.org/P19-1245) (Wilson & Mihalcea, ACL 2019)

• Predicting Human Activities from User-Generated Content (Wilson & Mihalcea, ACL 2019)

ACL

• Steven Wilson and Rada Mihalcea. 2019. Predicting Human Activities from User-Generated Content. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2572–2582, Florence, Italy. Association for Computational Linguistics.