Collective annotation of Wikipedia entities in web text

By Sayali Kulkarni, A. Singh, Ganesh Ramakrishnan, Soumen Chakrabarti. Knowledge Discovery and Data Mining, 2009

496 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Communication, Information Systems

View Paper: https://www.semanticscholar.org/paper/12f7b71324ee8e1796a9ef07af05b66674fe6af0

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Citations in the corpus

KORE: keyphrase overlap relatedness for entity disambiguation (2012, background): “has been shown in a wide variety of tasks: named entity disambiguation [23, 19, 16], general word sense disambiguation [31, 24, 26], query expansion for information retrieval [18, 25], Web-based information extraction [1], knowledge base population [9, 30], and more.”
Entity Linking meets Word Sense Disambiguation: a Unified Approach (2014, background): “To overcome this issue a novel class of approaches have been proposed (Kulkarni et al., 2009; Ratinov et al., 2011; Hoffart et al., 2011) that exploit global and local features.”
Robust Entity Linking via Random Walks (2014, methodology): “…also linked to a small subset of their full neighborhood in the KB. Disambiguation in this approach is done collectively on all mentions at the same time [6, 13, 16, 23]: they seek to ﬁnd a forest embedded in the constructed graph in which each mention remains linked to a single candidate entity.”
AIDA (2011, methodology): “This theme has been further pursued in Web mining, most notably, the work of [12] and [10].”
Natural Language Questions for the Web of Data (2012, methodology): “This generalized disambiguation problem is much more challenging than the more focused task of named entity disambiguation (NED).”
From TagME to WAT: a new entity annotator (2014, unknown): “Milne and Witten [13, 14], improved the original idea by proposing a new annotator that hinges on (i) the identi.cation in the input text of nonambiguous mentions C which links to only one entity; (ii) the introduction of a relatedness function rel(e1, e2) between two Wikipedia entities e1 and e”
Joint Coreference Resolution and Named-Entity Linking with Multi-Pass Sieves (2013, background): “Kulkarni et al. (2009) present an elegant collective disambiguation model, but do not exploit the syntactic nuances gleaned by within-document coreference resolution.”
YAGO2: A Spatially and Temporally Enhanced Knowledge Base from Wikipedia: Extended Abstract (2013, methodology): “The measure introduced by [30] and also used by [31] rates the relatedness of two entities based on the overlap of the set of incoming links in Wikipedia, and is defined as follows: inlink_coh(e1, e2) = 1 − log(max(|INe1 |, |INe2 |)) − log(|INe1 ∩ INe2 |) log(|N|) − log(min(|INe1 |, |INe2 |)) if > 0”
New Regularized Algorithms for Transductive Learning (2009, background): “As many other related algorithms [17] [12] [5], Adsorption assumes that the learning problem is given in a graph form, where examples or instances are represented as nodes or vertices and edges code similarity between examples.”
Discovering users' topics of interest on twitter: a first look (2010, background): “Other research has proceeded in this direction, leveraging Wikipedia as the knowledge base for entity disambiguation (and labeling) [11, 13, 14, 8].”
Local and Global Algorithms for Disambiguation to Wikipedia (2011, unknown): “…tasks including text classiﬁcation (Gabrilovich and Markovitch, 2007b; Chang et al., 2008), measuring semantic similarity between texts (Gabrilovich and Markovitch, 2007a), cross-document co-reference resolution (Finin et al., 2009; Mayﬁeld et al., 2009), and other tasks (Kulkarni et al., 2009).”
Robust named entity disambiguation with random walks (2018, methodology): “The actual disambiguation is done collectively on all mentions at the same time [7,14,19,28], by finding an embedded forest in the disambiguation graph in which each mention remains linked to just one of the candidates.”
A framework for benchmarking entity-annotation systems (2013, methodology): “IITB contains over a hundred of manually annotated texts drawn from popular Web pages about sport, entertainment, science and technology, and health [9].”
Re-ranking for joint named-entity recognition and linking (2013, background): “Previous work [3, 15, 9, 11] has considered collective classiﬁ-cation for entities, but never for the combination of NER and EL.”
Collective Entity Resolution with Multi-Focal Attention (2016, methodology): “Entity resolution (ER) is the task of mapping mentions of entities in text to corresponding records in a knowledge base (KB) (Bunescu and Pasca, 2006; Cucerzan, 2007; Kulkarni et al., 2009; Dredze et al., 2010; Hoffart et al., 2011; Hachey et al., 2013).”
Leveraging Deep Neural Networks and Knowledge Graphs for Entity Disambiguation (2015, methodology): “…and Witten, 2008b; Guo et al., 2013) to collective approaches that leverage the global topical coherence for joint disambiguation (Cucerzan, 2007; Kulkarni et al., 2009; Han et al., 2011; Ratinov et al., 2011; Hoffart et al., 2011; Cassidy et al., 2012; Shen et al., 2013; Liu et al., 2013; Huang…”
Evaluating Entity Linking with Wikipedia (2013, background): “The same approach is taken in wikification, where arbitrary phrases including names and general terms are matched to Wikipedia pages (Mihalcea and Csomai, 2007; Milne and Witten, 2008; Kulkarni et al., 2009; Ferragina and Scaiella, 2010).”
Learning Entity Representation for Entity Disambiguation (2013, methodology): “Another line of work focuses on collective disambiguation (Kulkarni et al., 2009; Han et al., 2011; Ratinov et al., 2011; Hoffart et al., 2011).”