Term Set Expansion based on Multi-Context Term Embeddings: an End-to-end Workflow

Autor:	Mamou, Jonathan, Pereg, Oren, Wasserblat, Moshe, Dagan, Ido, Goldberg, Yoav, Eirew, Alon, Green, Yael, Guskin, Shira, Izsak, Peter, Korat, Daniel
Rok vydání:	2018
Předmět:	Computer Science - Artificial Intelligence Computer Science - Computation and Language 68T50 I.2.7
Druh dokumentu:	Working Paper
Popis:	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). Comment: COLING 2018 System Demonstration paper
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1807.10104 Zobrazit plný text záznamu View this record from Arxiv