This paper presents a computational model of word learning that has roots in experimental literature and learns in real time with high precision from a small amount of data. In addition to incorporating external cues a la Yu & Ballard 2007, we give the learner the ability to test specific highly probable semantic hypotheses against new data. Performance is comparable to that of a more complex model (Frank et al. 2009) and better than that of a similar model (Fazly et al. 2010) that does not utilize hypothesis evaluation.