SBIR Phase I: Semantic Annotation of Hypertext and its Application in Event Mapping

The broader impact of this Small Business Innovation Research (SBIR) Phase I project will be to help applications of augmented reality, location-based search, and real-time live mapping of the world, and to build a digital twin of the earth. Event information is dynamic and hyperlocal. Thus it is important to ground the location and time of events to real GPS coordinates and universal time.

This creates technical challenges of entity disambiguation and resolution, and real-time processing event information to reflect last-minute changes. The proposed natural language grounding work also helps create a mirrorworld with information interlinked between the virtual internet and physical spaces and events.

This Small Business Innovation Research (SBIR) Phase I project will be the first to investigate neural language model pre-training over semi-structured hypertext on a web scale (55TB of compressed data monthly). This will greatly accelerate understanding of noisy web text, while the majority of research to date has been conducted on clean plain text only.

This project will also attack the challenge of machine reading with document-level annotations in a semi-supervised fashion, while the predominance of study has been carried out with more precise word-level annotation in a fully supervised way. The technical goal of this project is to create a scalable infrastructure that allows quick iterations of mining web scale data, and an ensemble of algorithms that are adapted to learning structured information over hypertext.

It is an unsolved challenge for most small businesses that in the past only the internet giants have attempted. The resulting machine learning algorithms will be capable of interpreting semi-structured web data, in contrast to typical structured annotation to understand hypertext.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.