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Named entity discovery (NED) is an important information retrieval problem that can be decomposed into two sub-problems.
Entity linking with effective acronym expansion, instance selection, and topic modeling. In IJCAI
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J-NERD: joint named entity recognition and disambiguation with rich linguistic features
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Joint learning of the embedding of words and entities for named entity disambiguation
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Graph-of-word and TW-IDF: New Approach to Ad Hoc IR. In Proceedings of the 22Nd ACM International Conference on Information & Knowledge Management
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AGDISTIS-graph-based disambiguation of named entities using linked data. In International Semantic Web Conference
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Fast and space-efficient entity linking for queries. In Proceedings of the Eighth ACM International Conference on Web Search and Data Mining
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Ikuya Yamada, Hiroyuki Shindo, Hideaki Takeda, and Yoshiyasu Takefuji. 2016 · 2016
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Deep joint entity disambiguation with local neural attention
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Neural cross-lingual entity linking
Avirup Sil, Gourab Kundu, Radu Florian, and Wael Hamza. 2017 · 2017
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Learning Distributed Representations of Texts and Entities from Knowledge Base
Ikuya Yamada, Hiroyuki Shindo, Hideaki Takeda, and Yoshiyasu Takefuji. 2017 · 2017
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DeepType: Multilingual Entity Linking by Neural Type System Evolution
Jonathan Raiman and Olivier Raiman. 2018 · 2018
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GraKeL: A Graph Kernel Library in Python
Giannis Siglidis, Giannis Nikolentzos, Stratis Limnios, Christos Giatsidis, Konstantinos Skianis, and Michalis Vazirgiannis. 2018 · 2018
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