Re-ranking for joint named-entity recognition and linking

By Avirup Sil, Alexander Yates. 2013

134 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

DKN: Deep Knowledge-Aware Network for News Recommendation (2018, methodology): “First, to distinguish knowledge entities in news content, we utilize the technique of entity linking [31, 36] to disambiguate mentions in texts by associating them with predefined entities in a knowledge graph.”
Neural Cross-Lingual Entity Linking (2017, methodology): “Most of the previous EL research (Cucerzan 2007; Ratinov et al. 2011; Sil and Yates 2013) have used Wikipedia as the target cata-log of entities, because of its coverage and frequent updates made by the community of users.”
Design Challenges for Entity Linking (2015, methodology): “We hypothesize that the inferior results of REL may be due to the in-completeness of Freebase triples, which makes it less robust than NGD.”
End-to-End Neural Entity Linking (2018, background): “One of the first attempts, (Sil and Yates, 2013) use a popular NER model to over-generate mentions and let the linking step to take the final decisions.”
Joint Entity Recognition and Disambiguation (2015, methodology): “JERL achieves precision@1 84.58 which is better than NereL.”
A Joint Neural Model for Information Extraction with Global Features (2020, methodology): “As a solution, some researchers propose joint inference and joint modeling methods to improve local prediction (Roth and Yih, 2004; Ji and Grishman, 2005; Ji et al., 2005; Sil and Yates, 2013; Li et al., 2014; Durrett and Klein, 2014; Miwa and Sasaki, 2014; Lu and Roth, 2015; Yang and Mitchell, 2016”
Entity Linking with a Knowledge Base: Issues, Techniques, and Solutions (2015, background): “Recently, some researchers [22], [23], [24] proposed to perform named entity recognition and entity linking jointly to make these two tasks reinforce each other, which is a promising direction especially for text where named entity recognition tools perform poorly (e.g., tweets).”
Joint Learning of Named Entity Recognition and Entity Linking (2019, background): “Sil and Yates (2013), Luo et al. (2015), and Nguyen et al. (2016) introduced models that do joint learning of NER and EL using hand-engineered features.”
A Joint Model for Entity Analysis: Coreference, Typing, and Linking (2014, background): “, 2005; Ratinov and Roth, 2009) and knowledge from Wikipedia (Kazama and Torisawa, 2007; Ratinov and Roth, 2009; Nothman et al., 2013; Sil and Yates, 2013).”