Discovering Content by Mining the Entity Web - Part 5 of 6

Deep Dhillon, CTO of Evri.com presents Evri's technology to UW students at the Paul G. Allen Center for Computer Science & Engineering. Talk abstract: Unstructured natural language text found in blogs, news and other web content is rich with semantic relations linking entities (people, places and things). At Evri, we are building a system which automatically reads web content similar to the way humans do. The system can be thought of as an army of 7th grade grammar students armed with a really large dictionary. The dictionary, or knowledge base, consists of relatively static information mined from structured and semi-structured publicly available information repositories like Freebase, Wikipedia, and Amazon. This large knowledge base is in turn used by a highly distributed search and indexing infrastructure to perform a deep linguistic analysis of many millions of documents ultimately culminating in a large set of semantic relationships expressing grammatical SVO style clause level relationships. This highly expressive, exacting, and scalable index makes possible a new generation of content discovery applications.

Category:

Science & Technology

Tags:

• content recommendation
• CS
• talks
• NLP