Abstract
There is a relationship between what we say and where we say it. Word embeddings are usually trained assuming that semantically-similar words occur within the same textual contexts. We investigate the extent to which semantically-similar words occur within the same geospatial contexts. We enrich a corpus of geolocated Twitter posts with physical data derived from Google Places and OpenStreetMap, and train word embeddings using the resulting geospatial contexts. Intrinsic evaluation of the resulting vectors shows that geographic context alone does provide useful information about semantic relatedness.- Anthology ID:
- E17-2016
- Volume:
- Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
- Month:
- April
- Year:
- 2017
- Address:
- Valencia, Spain
- Editors:
- Mirella Lapata, Phil Blunsom, Alexander Koller
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 99–104
- Language:
- URL:
- https://aclanthology.org/E17-2016
- DOI:
- Cite (ACL):
- Anne Cocos and Chris Callison-Burch. 2017. The Language of Place: Semantic Value from Geospatial Context. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers, pages 99–104, Valencia, Spain. Association for Computational Linguistics.
- Cite (Informal):
- The Language of Place: Semantic Value from Geospatial Context (Cocos & Callison-Burch, EACL 2017)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/E17-2016.pdf