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        <title>Building Location Embeddings from Physical Trajectories and Textual Representations</title>
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            <namePart type="given">Kam-Fai</namePart>
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    <abstract>Word embedding methods have become the de-facto way to represent words, having been successfully applied to a wide array of natural language processing tasks. In this paper, we explore the hypothesis that embedding methods can also be effectively used to represent spatial locations. Using a new dataset consisting of the location trajectories of 729 students over a seven month period and text data related to those locations, we implement several strategies to create location embeddings, which we then use to create embeddings of the sequences of locations a student has visited. To identify the surface level properties captured in the representations, we propose a number of probing tasks such as the presence of a specific location in a sequence or the type of activities that take place at a location. We then leverage the representations we generated and employ them in more complex downstream tasks ranging from predicting a student’s area of study to a student’s depression level, showing the effectiveness of these location embeddings.</abstract>
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%0 Conference Proceedings
%T Building Location Embeddings from Physical Trajectories and Textual Representations
%A Biester, Laura
%A Banea, Carmen
%A Mihalcea, Rada
%Y Wong, Kam-Fai
%Y Knight, Kevin
%Y Wu, Hua
%S Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing
%D 2020
%8 December
%I Association for Computational Linguistics
%C Suzhou, China
%F biester-etal-2020-building
%X Word embedding methods have become the de-facto way to represent words, having been successfully applied to a wide array of natural language processing tasks. In this paper, we explore the hypothesis that embedding methods can also be effectively used to represent spatial locations. Using a new dataset consisting of the location trajectories of 729 students over a seven month period and text data related to those locations, we implement several strategies to create location embeddings, which we then use to create embeddings of the sequences of locations a student has visited. To identify the surface level properties captured in the representations, we propose a number of probing tasks such as the presence of a specific location in a sequence or the type of activities that take place at a location. We then leverage the representations we generated and employ them in more complex downstream tasks ranging from predicting a student’s area of study to a student’s depression level, showing the effectiveness of these location embeddings.
%U https://aclanthology.org/2020.aacl-main.44
%P 425-434

Markdown (Informal)

[Building Location Embeddings from Physical Trajectories and Textual Representations](https://aclanthology.org/2020.aacl-main.44) (Biester et al., AACL 2020)

• Building Location Embeddings from Physical Trajectories and Textual Representations (Biester et al., AACL 2020)

ACL

• Laura Biester, Carmen Banea, and Rada Mihalcea. 2020. Building Location Embeddings from Physical Trajectories and Textual Representations. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, pages 425–434, Suzhou, China. Association for Computational Linguistics.