Semantic Multimedia Information Retrieval Based on Contextual Descriptions

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GERBIL - Benchmarking Named Entity Recognition and Linking consistently (2017, methodology): “KEA NER/NED: This approach is the successor of the approach introduced in [58] which is based on a fine-granular context model taking into account heterogeneous text sources as well as text created by automated multimedia analysis.”
End-to-End Neural Entity Linking (2018, unknown): “We compare with popular and state-of-the-art EL and ED public systems, e.g. WAT (Piccinno and Ferragina, 2014), Dbpedia Spotlight (Mendes et al., 2011), NERD-ML (van Erp et al., 2013), KEA (Steinmetz and Sack, 2013), AIDA (Hoffart et al., 2011), FREME, FOX (Speck and Ngomo, 2014), PBOH (Ganea et al.”
Robust named entity disambiguation with random walks (2018, background): “The most commonly used local features include lexical features such as bag-of-words [2], keyphrases [15], n-grams [37], or semantic word embeddings [47] extracted or derived from the surrounding context, and statistical features such as the probability of entities given a mention from a knowledge gr”