In this thesis we explore the compositionality of particle verbs using distributional similarity and pre-trained word embeddings. We investigate the compositionality of 100 pairs of particle verbs with their base verbs. The ranking of our findings are compared to a ranking of human ratings on compositionality. In our distributional approach we use features such as context window size, content words, and only use particle verbs with one word sense. We then compare the distributional approach to a ranking done with pre-trained word embeddings. While none of the results are statistically significant, it is shown that word embeddings are not automatically superior to the more traditional distributional approach.