Enriching Word Embeddings with Temporal and Spatial Information

Hongyu Gong, Suma Bhat, Pramod Viswanath


Abstract
The meaning of a word is closely linked to sociocultural factors that can change over time and location, resulting in corresponding meaning changes. Taking a global view of words and their meanings in a widely used language, such as English, may require us to capture more refined semantics for use in time-specific or location-aware situations, such as the study of cultural trends or language use. However, popular vector representations for words do not adequately include temporal or spatial information. In this work, we present a model for learning word representation conditioned on time and location. In addition to capturing meaning changes over time and location, we require that the resulting word embeddings retain salient semantic and geometric properties. We train our model on time- and location-stamped corpora, and show using both quantitative and qualitative evaluations that it can capture semantics across time and locations. We note that our model compares favorably with the state-of-the-art for time-specific embedding, and serves as a new benchmark for location-specific embeddings.
Anthology ID:
2020.conll-1.1
Volume:
Proceedings of the 24th Conference on Computational Natural Language Learning
Month:
November
Year:
2020
Address:
Online
Editors:
Raquel Fernández, Tal Linzen
Venue:
CoNLL
SIG:
SIGNLL
Publisher:
Association for Computational Linguistics
Note:
Pages:
1–11
Language:
URL:
https://aclanthology.org/2020.conll-1.1
DOI:
10.18653/v1/2020.conll-1.1
Bibkey:
Cite (ACL):
Hongyu Gong, Suma Bhat, and Pramod Viswanath. 2020. Enriching Word Embeddings with Temporal and Spatial Information. In Proceedings of the 24th Conference on Computational Natural Language Learning, pages 1–11, Online. Association for Computational Linguistics.
Cite (Informal):
Enriching Word Embeddings with Temporal and Spatial Information (Gong et al., CoNLL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.conll-1.1.pdf
Optional supplementary material:
 2020.conll-1.1.OptionalSupplementaryMaterial.zip
Code
 HongyuGong/EnrichedWordRepresentation