This paper proposes a computational lexical entry acquisition model based on a representation model of the mental lexicon. The proposed model acquires lexical entries from a raw corpus by unsupervised learning like human. The model is composed of full-form and morpheme acquisition modules. In the full-from acquisition module, core full-forms are automatically acquired according to the frequency and recency thresholds. In the morpheme acquisition module, a repeatedly occurring substring in different full-forms is chosen as a candidate morpheme. Then, the candidate is corroborated as a morpheme by using the entropy measure of syllables in the string. The experimental results with a Korean corpus of which size is about 16 million full-forms show that the model successively acquires major full-forms and morphemes with the precision of 100% and 99.04%, respectively.