1 research outputs found
Improving Context-Aware Semantic Relationships in Sparse Mobile Datasets
Traditional semantic similarity models often fail to encapsulate the external
context in which texts are situated. However, textual datasets generated on
mobile platforms can help us build a truer representation of semantic
similarity by introducing multimodal data. This is especially important in
sparse datasets, making solely text-driven interpretation of context more
difficult. In this paper, we develop new algorithms for building external
features into sentence embeddings and semantic similarity scores. Then, we test
them on embedding spaces on data from Twitter, using each tweet's time and
geolocation to better understand its context. Ultimately, we show that applying
PCA with eight components to the embedding space and appending multimodal
features yields the best outcomes. This yields a considerable improvement over
pure text-based approaches for discovering similar tweets. Our results suggest
that our new algorithm can help improve semantic understanding in various
settings.Comment: 6 pages, 7 figure