UC Irvine

In this paper, I look at applications to the INNO Fund ACORN, a biannualprogram that gives fifty thousand dollar grants to start-up companies that show promise. I attempt to answer many of the common questions that entrepreneurs might have about applying to receive grant funding by using many of the advanced software tools that have been developed for natural language processing purposes. This study would be useful to anyone interested in raising money for a company, as well as someone looking to better understand the field of natural language processing, with applications in business. A transformer encoder-decoder model was used to efficiently and effectively summarize applications. This model was necessary to summarize applications through the synthesis of new sentences with novel words and phrases, as opposed to simply extracting sentences containing the most common words in the document. It was found that companies tend to receive funding more often if they have larger teams, that a founding team's tenacity is an important factor in attempting to raise money multiple times if the original attempt fails, and that many founders are serial v entrepreneurs, meaning they often are involved in the creation of multiple start-up companies throughout their career. Finally, Latent Dirichlet Allocation (LDA) was used to automatically classify the topics of all of the applications in the dataset with high confidence.