Building Location Embeddings from Physical Trajectories and Textual Representations

Laura Biester, Carmen Banea, Rada Mihalcea


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.
Anthology ID:
2020.aacl-main.44
Volume:
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
Month:
December
Year:
2020
Address:
Suzhou, China
Venue:
AACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
425–434
Language:
URL:
https://aclanthology.org/2020.aacl-main.44
DOI:
Bibkey:
Cite (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.
Cite (Informal):
Building Location Embeddings from Physical Trajectories and Textual Representations (Biester et al., AACL 2020)
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