• Project Title: Determining the Most Efficient Methods By Which Computers Analyze Metaphor

  • BASIS Advisor: Ms. Bhattacharya

  • Internship Location: Computer History Museum

  • Onsite Mentor: Dr. Jelena Mitrovic

Artificial Intelligence is playing an increasingly large role in society, and has proven itself to be an extremely efficient tool in nearly every field that it is applied to. In order for AI to be useful, it must be accessible. For AI to be accessible, it needs to be able to understand the people who utilize it. My Senior Project specifically addresses human metaphor and how computers interpret such an abstract figure of speech. I’m conducting my research at the Computer History Museum, and I will be analyzing three different methods by which computers identify phrases as literal or metaphorical. The first method counts phrases as metaphors or not, based on how often a noun and its hyponyms/hypernyms occur with the adjective. The second compares adjectives and verbs to the paired noun, and if the noun is abstract compared to a concrete adjective, it is most likely a metaphor. The third uses a random-forest-classifier to create a data tree and select the most favorable branch. As of now, I don’t have a clear prediction for which method will be the fastest or most accurate, but I believe that the results will be very similar between methods. By finding the most accurate method, I feel that I can help increase the general ease with which computers understand humans.