Term Set Expansion based on Multi-Context Term Embeddings: an End-to-end Workflow
This addresses the need for efficient term set expansion in domain-specific applications like recruitment and defect tracking, though 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 by presenting SetExpander, an end-to-end workflow system that simplifies extraction of domain-specific classes, with real-life applications in recruitment and defect resolution systems.
We present SetExpander, a corpus-based system for expanding a seed set of terms into a more complete set of terms that belong to the same semantic class. SetExpander implements an iterative end-to end workflow for term set expansion. 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 for solving real-life use cases including integration in 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).