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The words-as-classifiers model of grounded lexical semantics learns asemantic fitness score between physical entities and the words that are used todenote those entities. In this paper, we explore how such a model canincrementally perform composition and how the model can be unified with adistributional representation. For the latter, we leverage the classifiercoefficients as an embedding. For composition, we leverage the underlyingmechanics of three different classifier types (i.e., logistic regression,decision trees, and multi-layer perceptrons) to arrive at a several systematicapproaches to composition unique to each classifier including both denotationaland connotational methods of composition. We compare these approaches to eachother and to prior work in a visual reference resolution task using the refCOCOdataset. Our results demonstrate the need to expand upon existing compositionstrategies and bring together grounded and distributional representations.

Composing and Embedding the Words-as-Classifiers Model of Grounded Semantics