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ENTYFI: A System for Fine-grained Entity Typing in Fictional Texts

Cuong Xuan Chu, Simon Razniewski, Gerhard Weikum
2020 Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations   unpublished
We present ENTYFI, a web-based system for fine-grained typing of entity mentions in fictional texts.  ...  Users can exploit the richness and diversity of these reference type systems for fine-grained supervised typing, in addition, they can choose among and combine four other typing modules: pre-trained real-world  ...  However, these systems all predict only a few coarse and real world types (4-16 types). ENTYFI is the first attempt to entity typing at a fine-grained level for fictional texts.  ... 
doi:10.18653/v1/2020.emnlp-demos.14 fatcat:bkwocausuvgovaqhnsexn3cqei

Knowledge extraction from fictional texts [article]

Cuong Xuan Chu, Universität Des Saarlandes
Knowledge extraction from text is a key task in natural language processing, which involves many sub-tasks, such as taxonomy induction, named entity recognition and typing, relation extraction, knowledge  ...  State-of-the-art methods for knowledge extraction make assumptions on entity-class, subclass and entity-entity relations that are often invalid for fictional domains.  ...  , called ENTYFI (fined-grained ENtity TYping on FIctional texts).  ... 
doi:10.22028/d291-36107 fatcat:hdzlcbxc5ngr3hbeotkcdpbjqm