When applied to novel domains such as legal, medical, and hacker chatter, the out-of-box accuracy of NLP systems trained on news and other general-purpose datasets leaves much to be desired. What matters is how well a system performs on your data, and how easy it is to extract the information you need with minimal developer effort. In this webinar, we’ll introduce three new customization techniques for achieving your specific text processing goals with Rosette:
1. Rapid development of custom entity & event extraction models with active learning, which reduces the number of annotated samples needed by about 75%.
2. Resolving entity mentions to your knowledge base. With our custom database connector, leverage the power of contextual disambiguation for domain-specific entities of any type.
3. Building custom text processing workflows to weave together multiple NLP functions with custom logic. For example, run entity extraction on an Arabic document to pull out key people, places, and organizations, then subsequently translate these entity names into English, all via a single API call.