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Building Location Embeddings from Physical Trajectories and Textual Representations

Biester, Laura; Banea, Carmen; Mihalcea, Rada

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<oai_dc:dc xmlns:dc="" xmlns:oai_dc="" xmlns:xsi="" xsi:schemaLocation="">
  <dc:creator>Biester, Laura</dc:creator>
  <dc:creator>Banea, Carmen</dc:creator>
  <dc:creator>Mihalcea, Rada</dc:creator>
  <dc:description>Code for building and evaluating location embeddings for the 2020 AACL-IJCNLP paper "Building Location Embeddings from Physical Trajectories and Textual Representations."

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.

Contact: Please contact Laura Biester ( with questions.</dc:description>
  <dc:title>Building Location Embeddings from Physical Trajectories and Textual Representations</dc:title>
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