Term Set Expansion based NLP Architect by Intel AI Lab
This addresses the need for efficient term set expansion in NLP applications, but it appears incremental as it builds on existing corpus-based methods.
The paper tackles the problem of expanding a seed set of terms into a more complete semantic class, resulting in a system called SetExpander that simplifies domain-specific term extraction and has been successfully integrated into real-life use cases like recruitment and defect resolution systems.
We present SetExpander, a corpus-based system for expanding a seed set of terms into amore complete set of terms that belong to the same semantic class. SetExpander implements an iterative end-to-end workflow. It enables users to easily select a seed set of terms, expand it, view the expanded set, validate it, re-expand the validated set and store it, thus simplifying the extraction of domain-specific fine-grained semantic classes.SetExpander has been used successfully in real-life use cases including integration into an automated recruitment system and an issues and defects resolution system. A video demo of SetExpander is available at https://drive.google.com/open?id=1e545bB87Autsch36DjnJHmq3HWfSd1Rv (some images were blurred for privacy reasons)