We are excited to announce the first in a new series of posts and a brand new initiative: Data Community DC Videos! We are going to film and publish online videos (and separate audio, resources permitting) as many talks from Data Community DC meetups as possible. Yes, we want you to experience the events in person, but realize that not everyone who wants to be a part of our community can attend every single event. To kick this off, we have a fantastic video of Dr. Jesse English passionately discussing a brand new, open source framework, WIMs (Weakly Inferred Meanings), a novel approach to creating structured meaning representations for semantic analyses. Whereas a TMR (text meaning representation) requires a large, domain-specific knowledge base and significant computation times, WIMs cover a limited scope of possible relationships. The limitation is intentional, and allows for better performance-- but still carries enough relationships for most applications. Additionally, the creation of a bespoke knowledge base and microtheory is not required, the novel pattern matching technique means that available ontologies like WordNet provide enough coverage. WIMs are Open Source and available now, and are truly a break through in semantic processing.
Dr. Jesse English holds a PhD in computer science from UMBC and has specialized in natural language processing, machine learning and machine reading. As the Chief Science Officer at Unbound Concepts, Jesse's focused on automatic extraction of semantically rich meaning from literature, and application of that knowledge to the company's big-data driven machine learning algorithm. Before his work at Unbound Concepts, Jesse worked as a research associate at UMBC, focusing on automatically bridging the knowledge acquisition bottleneck through machine reading, as well as developing agent-based conversation systems.