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        <title>ENTYFI: A System for Fine-grained Entity Typing in Fictional Texts</title>
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        <namePart type="given">Cuong</namePart>
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            <namePart type="given">Qun</namePart>
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    <abstract>Fiction and fantasy are archetypes of long-tail domains that lack suitable NLP methodologies and tools. We present ENTYFI, a web-based system for fine-grained typing of entity mentions in fictional texts. It builds on 205 automatically induced high-quality type systems for popular fictional domains, and provides recommendations towards reference type systems for given input 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 models, unsupervised dependency-based typing, knowledge base lookups, and constraint-based candidate consolidation. The demonstrator is available at: https://d5demos.mpi-inf.mpg.de/entyfi.</abstract>
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    <identifier type="doi">10.18653/v1/2020.emnlp-demos.14</identifier>
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%0 Conference Proceedings
%T ENTYFI: A System for Fine-grained Entity Typing in Fictional Texts
%A Chu, Cuong Xuan
%A Razniewski, Simon
%A Weikum, Gerhard
%Y Liu, Qun
%Y Schlangen, David
%S Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2020
%8 October
%I Association for Computational Linguistics
%C Online
%F chu-etal-2020-entyfi
%X Fiction and fantasy are archetypes of long-tail domains that lack suitable NLP methodologies and tools. We present ENTYFI, a web-based system for fine-grained typing of entity mentions in fictional texts. It builds on 205 automatically induced high-quality type systems for popular fictional domains, and provides recommendations towards reference type systems for given input 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 models, unsupervised dependency-based typing, knowledge base lookups, and constraint-based candidate consolidation. The demonstrator is available at: https://d5demos.mpi-inf.mpg.de/entyfi.
%R 10.18653/v1/2020.emnlp-demos.14
%U https://aclanthology.org/2020.emnlp-demos.14
%U https://doi.org/10.18653/v1/2020.emnlp-demos.14
%P 100-106

Markdown (Informal)

[ENTYFI: A System for Fine-grained Entity Typing in Fictional Texts](https://aclanthology.org/2020.emnlp-demos.14) (Chu et al., EMNLP 2020)

• ENTYFI: A System for Fine-grained Entity Typing in Fictional Texts (Chu et al., EMNLP 2020)

ACL

• Cuong Xuan Chu, Simon Razniewski, and Gerhard Weikum. 2020. ENTYFI: A System for Fine-grained Entity Typing in Fictional Texts. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 100–106, Online. Association for Computational Linguistics.