Building Location Embeddings from Physical Trajectories and Textual Representations (2020.aacl-main)
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| Challenge: | Using a dataset consisting of the location trajectories of 729 students over a seven month period, we investigate whether embeddings can represent aspects such as location presence or location functionality. |
| Approach: | They propose to use location embeddings to generate embeddables of sequences of locations a student has visited to identify surface properties captured in the representations. |
| Outcome: | The proposed models can be used to predict depression levels and area of study, and can be applied to complex tasks such as predicting area of studies and depression levels. |
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| Challenge: | a number of studies have focused on detecting named entities in written language. |
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Leveraging the Structure of Pre-trained Embeddings to Minimize Annotation Effort (2024.naacl-long)
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Embeddings in Natural Language Processing (2020.coling-tutorials)
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| Challenge: | Text embeddings are a fundamental component in many NLP tasks, but their interpretation and explanation remain challenging. |
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| Challenge: | Existing word embeddings are high-dimensional and consume considerable computational resources. |
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Revealing the Numeracy Gap: An Empirical Investigation of Text Embedding Models (2026.findings-eacl)
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Do Trajectories Encode Verb Meaning? (2022.naacl-main)
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| Challenge: | Distributional models learn representations of words from text but lack grounding or the linking of text to the non-linguistic world. |
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Are the Best Multilingual Document Embeddings simply Based on Sentence Embeddings? (2023.findings-eacl)
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