EAPSI: Learning Semantic Decomposition in Support of Commonsense Reasoning

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Commonsense reasoning is the ability for humans to use everyday facts about the world to support planning. To create a story about a bank robbery, one must have knowledge of what a bank is, why one would wish to rob it, etc. Artificial intelligence (AI) agents can learn a lot about the world by reading existing text sources, such as Wikipedia or narrative. However, the information learned so far from these sources tends to be explicit, such as, person A was born at location B. This project will investigate methods that can be used to learn implicit knowledge from text such as spatial or temporal properties of concepts. This knowledge can then support many different current challenges in AI, such as narrative generation. This project will be conducted at Toyota Technological University under the mentorship of Dr. Makoto Miwa. The collaboration provides access to unique data that will enable new insights into how humans process everyday concepts.Current work on information extraction tends to learn propositions that are explicitly encoded in text. Implicit information is equally or more important in giving cognitive agents the ability to reason about the world. Under the supervision of Dr. Miwa, I will apply machine learning and analogical techniques to the task of associative concept learning. I will specifically look at low level qualia related attributes including temporality, space, sentiment, aesthetics, etc. I will use various corpora in support of this task. Dr. Miwa, along with his research group at Toyota Technological University, are leaders in information extraction techniques. Subsequently, Dr. Miwa's insight and expertise will be an invaluable aid in developing techniques for machine learning implicit knowledge.This award, under the East Asia and Pacific Summer Institutes program, supports summer research by a U.S. graduate student and is jointly funded by NSF and the Japan Society for the Promotion of Science.