Date of Original Version
Proceedings of d Annual Meeting of the Association for Computational Linguistics (Short Papers)
Copyright 2014 Association for Computational Linguistics
Abstract or Description
We introduce a model for incorporating contextual information (such as geography) in learning vector-space representations of situated language. In contrast to approaches to multimodal representation learning that have used properties of the object being described (such as its color), our model includes information about the subject (i.e., the speaker), allowing us to learn the contours of a word’s meaning that are shaped by the context in which it is uttered. In a quantitative evaluation on the task of judging geographically informed semantic similarity between representations learned from 1.1 billion words of geo-located tweets, our joint model outperforms comparable independent models that learn meaning in isolation.
Proceedings of d Annual Meeting of the Association for Computational Linguistics (Short Papers), 828-834.