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Building Location Embeddings from Physical Trajectories and Textual Representations

Biester, Laura; Banea, Carmen; Mihalcea, Rada

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  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.4479440", 
  "language": "eng", 
  "title": "Building Location Embeddings from Physical Trajectories and Textual Representations", 
  "issued": {
    "date-parts": [
  "abstract": "<p>Code for building and evaluating location embeddings for the 2020 AACL-IJCNLP&nbsp;paper &quot;Building Location Embeddings from Physical Trajectories and Textual Representations.&quot;</p>\n\n<p><strong>Abstract:&nbsp;</strong>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&#39;s area of study to a student&#39;s depression level, showing the effectiveness of these location embeddings.</p>\n\n<p><strong>Contact:</strong>&nbsp;Please contact Laura Biester ( with questions.</p>", 
  "author": [
      "family": "Biester, Laura"
      "family": "Banea, Carmen"
      "family": "Mihalcea, Rada"
  "id": "4479440", 
  "event-place": "Virtual", 
  "type": "article", 
  "event": "1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing (AACL-IJCNLP 2020)"
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