The meaning of words in natural language depends crucially on context. However, most neuroimaging studies of word meaning use isolated words and isolated sentences with little context. Because the brain may process natural language differently from how it processes simplified stimuli, there is a pressing need to determine whether prior results on word meaning generalize to natural language. We investigated this issue by directly comparing the brain representation of semantic information across four conditions that vary in context. fMRI was used to record human brain activity while four subjects (two female) read words presented in four different conditions: narratives (Narratives), isolated sentences (Sentences), blocks of semantically similar words (Semantic Blocks), and isolated words (Single Words). Using a voxelwise encoding model approach, we find two clear and consistent effects of increasing context. First, stimuli with more context (Narratives, Sentences) evoke brain responses with substantially higher SNR across bilateral visual, temporal, parietal, and prefrontal cortices compared to stimuli with little context (Semantic Blocks, Single Words). Second, increasing context increases the representation of semantic information across bilateral temporal, parietal, and prefrontal cortices at the group level. However, in individual subjects, only natural language stimuli (Narratives) consistently evoke widespread representation of semantic information across the cortical surface. These results show that context has large effects on both the quality of neuroimaging data and on the representation of meaning in the brain, and they imply that the results of neuroimaging studies that use stimuli with little context may not generalize well to the natural regime.