Human memory does not simply function like an information storage disk; instead, it flexibly reorganizes information. This flexibility can sometimes produce false memories of items related to those actually encountered–a possible byproduct of an adaptive memory system that enables generalization across related items or experiences. In the Deese-Roediger-McDermott (DRM) task, participants often falsely remember seeing words that are semantically related to presented words. Here, we propose and test a new model of lexical (false) memory that predicts these errors, made possible by integrating (i) theories of memory that posit encoding of verbatim and gist-level information with (ii) a computational framework adapted from the perceptual false memory literature, and (iii) semantic relatedness measures from word embeddings analysis of large-scale text corpora. This Lexical Target Confusability Competition (Lexical TCC) model successfully predicts false recognition in the DRM task, with implications for understanding how and when the mind produces false memories.