Composing and Embedding theWords-as-Classifiers Model of Grounded Semantics
Table 2: Experiment 3 results: Spearman Correlation between the cosine similarities of wac, GloVe, and the two combined.
['[BOLD] model', '[BOLD] WordSim-353', '[BOLD] SimLex-999']
S5SS4SSS0Px3 Results The results show that, when combined with embeddings trained on large amounts of text such as GloVe, the wac coefficient vectors can contribute useful information derived from the visual features that the GloVe embeddings don’t have. We conjecture that wac does not work well on its own because the words are trained independently, and the wac model assumes that words trained on visual representations are concrete, whereas many words are abstract, the semantics of which is learned in lexical context, such as distributional embeddings. These results show that wac is a potential model for bridging grounded and distributional approaches to lexical semantics, which we will explore further in future work.