Entity Linking: Finding Extracted Entities in a Knowledge Base
By Delip Rao, Paul McNamee, Mark Dredze. 2013
224 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Management Science and Operations Research, Molecular Biology, Information Extraction, Knowledge Graphs & Representation
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Le Song
- Jie Tang
- Mohit Bansal
- William W. Cohen
- Hanjun Dai
- Luke S. Zettlemoyer
- Yoshua Bengio
- Zhilin Yang
In Management Science and Operations Research
- Krishnaprasad Thirunarayan
- Leon Derczynski
- Amit P. Sheth
- Jason Eisner
- Giuseppe Rizzo
- Raphael Troncy
- Pablo N. Mendes
- Kalina Bontcheva
In Molecular Biology
- Alessandro Raganato
- Amit P. Sheth
- Andrea Moro
- Leon Derczynski
- Jason Eisner
- Giuseppe Rizzo
- Raphael Troncy
- Kalina Bontcheva
In Information Extraction
- Roberto Navigli
- Alessandro Raganato
- Leon Derczynski
- Marieke van Erp
- Amit P. Sheth
- Andrea Moro
- Jason Eisner
- Le Song
In Knowledge Graphs & Representation
- Amit P. Sheth
- Jason Eisner
- Pablo N. Mendes
- Zornitsa Kozareva
- Roberto Navigli
- Le Song
- Zhilin Yang
- Jie Tang
Citations in the corpus
- Learning How to Ask: Querying LMs with Mixtures of Soft Prompts (2021, methodology): “, 2016), entity linking (Rao et al., 2013) and relation extraction (Sorokin and Gurevych, 2017).”
- Entity Linking meets Word Sense Disambiguation: a Unified Approach (2014, background): “, 2013), has favoured the emergence of new tasks, such as Entity Linking (EL) (Rao et al., 2013), and opened up new possibilities for tasks such as Named Entity Disambiguation (NED) and Wikification.”
- Multi-Modal Bayesian Embeddings for Learning Social Knowledge Graphs (2015, methodology): “Different from conventional keyword extraction methods [Liu et al., 2009; Mundy and Thornthwaite, 2007; Matsuo and Ishizuka, 2004; Rao et al., 2013], our method is based on topic models and embedding learning.”
- Analysis of named entity recognition and linking for tweets (2014, background): “A follow-up step to NER is Named Entity Linking (NEL), which links entity mentions within the same document (also known as entity disambiguation) [4], or in other resources (also known as entity resolution) [5].”
- Implicit Entity Linking in Tweets (2016, background): “The explicit entity linking (EL) task can be defined as “matching a textual entity mention, possibly identified by a named entity recognizer, to a KB entry, such as a Wikipedia page that is a canonical entry for that entity” [16].”
- Variational Reasoning for Question Answering with Knowledge Graph (2017, background): “For text questions, broad matching techniques (e.g. hand-craft rules, regular expressions, edit distance, etc.) are widely used for entity recognition [14].”