Entity Disambiguation for Knowledge Base Population

By Mark Dredze, Paul McNamee, Delip Rao, Adam Gerber, Timothy W. Finin. International Conference on Computational Linguistics, 2010

353 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/fc165664f4c8a51bd048cf2ab42a96662d0dfd12

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Citations in the corpus

Knowledge base completion via search-based question answering (2014, methodology): “For this, we use standard entity linkage techniques, such as [6], which takes into account the lexical context of each mention, and [7], which takes into account other entities near the given mention, using joint inference.”
Understandable Big Data: A survey (2015, background): “It aims to identify an entry in a given Knowledge Base (KB) to which an entity mention in a document refers to [74,75].”
Evaluating Entity Linking with Wikipedia (2013, methodology): “The ﬁrst large set of manually annotated named entity linking data was prepared by the National Institute of Standards and Technologies ( nist ) as part of the Knowledge Base Population ( kbp ) shared task at the 2009 Text Analysis Conference ( tac ) (McNamee et al., 2010).”
Robust named entity disambiguation with random walks (2018, methodology): “Unsupervised approaches [2,3] commonly use the cosine similarity of feature vectors to measure the compatibility, while supervised approaches [22,25,35,37,34,8] exploit various classifiers trained on labeled datasets (often derived from Wikipedia) to predict the compatibility.”
Entity Linking with a Knowledge Base: Issues, Techniques, and Solutions (2015, methodology): “…different methods have been utilized, including dot-product [22], [62], [75], cosine similarity [44], [59], [62], [65], [66], [68], [70], [76], [79], [83], [90], [94], Dice coefficient [83], word overlap [58], [96], KL divergence [58], n-gram based measure [58], and Jaccard similarity [62].”
Multilingual Autoregressive Entity Linking (2021, unknown): “Entity Linking (EL, Hoffart et al., 2011; Dredze et al., 2010; Bunescu and Pa¸sca, 2006; Cucerzan, 2007) is an important task in NLP, with plenty of applications in multiple domains, spanning Question Answering (De Cao et al., 2019; Nie et al., 2019; Asai et al., 2020), Dialogue (Bordes et al.,…”
Robust Entity Linking via Random Walks (2014, methodology): “Unsupervised approaches [2,3] commonly use the cosine similarity of feature vectors to measure the compatibility, while supervised approaches [7,18,21,25–27] exploit various classiﬁers trained on labeled datasets (often derived from Wikipedia) to predict the compatibility.”
Implicit Entity Linking in Tweets (2016, background): “The word-level entity linking has been studied extensively for text like Wikipedia and News [2] [5] [9] [13] [14]; however, these approaches have proved to be ineffective on short and noisy text like tweets [4] [6].”
Knowledge Base Population: Successful Approaches and Challenges (2011, background): “• Require Entity Salience Ranking Some of these queries represent salient entities and so using web popularity rank (e.g. ranking/hit counts of Wikipedia pages from search engine) can yield correct answers in most cases (Bysani et al., 2010; Dredze et al., 2010).”
MMKG: Multi-Modal Knowledge Graphs (2019, unknown): “Named-entity linking (NEL) [7,12] is the task of linking a named-entity mention from a text to an entity in a knowledge graph.”
Collective entity linking in web text: a graph-based method (2011, background): “Zheng et al. [23], Dredze et al. [5] and Zhou et al. [24] employed the learning-to-rank techniques, which can take into account the relative rank between the candidate entities.”
Collective Entity Resolution with Multi-Focal Attention (2016, background): “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).”
A Joint Model for Entity Analysis: Coreference, Typing, and Linking (2014, background): “Most entity linking systems operate in two distinct phases (Cucerzan, 2007; Milne and Witten, 2008; Dredze et al., 2010; Ratinov et al., 2011).”
Named Entity Recognition for Chinese Social Media with Jointly Trained Embeddings (2015, background): “Named entity recognition (NER), and more generally the task of mention detection 1 , is an essential component of information extraction technologies: the ﬁrst step before tasks such as relation extraction (Bunescu and Mooney, 2005) and entity linking (Dredze et al., 2010; Ratinov et al., 2011).”
Overview of the TAC 2010 Knowledge Base Population Track (2010, background): “Many new and interesting approaches have been published by participants at major NLP conferences (Dredze et al., 2010; Zhang et al., 2010; Zheng et al., 2010; Chen and Ji, 2011; Gottipati and Jiang, 2011; Han and Sun, 2011; Ploch, 2011; Zhang et al., 2011; Zhang et al., 2011).”
Collective Entity Disambiguation with Structured Gradient Tree Boosting (2018, background): “…role in the language understanding pipeline, underlying a variety of downstream applications such as relation extraction (Mintz et al., 2009; Riedel et al., 2010), knowledge base population (Ji and Grishman, 2011; Dredze et al., 2010), and question answering (Berant et al., 2013; Yih et al., 2015).”
Joint Entity Linking with Deep Reinforcement Learning (2019, methodology): “en the mention’s surrounding words and the entity pro•le in the reference KB. Various methods have been proposed to model mention’s local context ranging from binary classi•cation [25] to rank models [4, 11]. In these methods, a large number of hand-designed features are applied. For some marginal m”