Evaluating Entity Linking with Wikipedia

Hachey, Ben; Radford, Will; Nothman, Joel; Honnibal, Matthew; Curran, James

doi:10.1016/j.artint.2012.04.005

Cited by 211 publications

(183 citation statements)

References 28 publications

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“…for unsupervised coreference resolution. Most approaches that use Wikipedia as a resource for disambiguation focus on named entities (Bunescu and Paşca, 2006;Cucerzan, 2007;Dredze et al, 2010;Hachey et al, 2013;Hoffart et al, 2011), while only a few disambiguate common and proper nouns like us (Csomai and Mihalcea, 2008;Milne and Witten, 2008;Zhou et al, 2010;Ratinov et al, 2011;Cheng and Roth, 2013). We build upon our previous Markov Logic based approach for joint concept disambiguation and clustering (Fahrni and Strube, 2012).…”

Section: Related Workmentioning

confidence: 99%

A Latent Variable Model for Discourse-aware Concept and Entity Disambiguation

Fahrni

Strube

2014

Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics

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Section: Related Workmentioning

confidence: 99%

A Latent Variable Model for Discourse-aware Concept and Entity Disambiguation

Fahrni

Strube

2014

Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics

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“…The extraction of the triples includes six tasks: named entity recognition, part of speech tagging, dependency parsing, triple extraction, entity linkage (which maps mentions of proper nouns and their co-references to the corresponding entities in Freebase) and relation linkage. We use three information extraction (IE) tools (Angeli et al (2014), , MITIE 3 ) for the first four tasks, and develop a method similar to Hachey et al (2013) for the last two tasks of entity linkage and relation linkage.…”

Section: Accuracymentioning

confidence: 99%

Incorporating Trustiness and Collective Synonym/Contrastive Evidence into Taxonomy Construction

Anh¹,

Kim²,

Ng³

2015

Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Taxonomy plays an important role in many applications by organizing domain knowledge into a hierarchy of is-a relations between terms. Previous works on the taxonomic relation identification from text corpora lack in two aspects: 1) They do not consider the trustiness of individual source texts, which is important to filter out incorrect relations from unreliable sources. 2) They also do not consider collective evidence from synonyms and contrastive terms, where synonyms may provide additional supports to taxonomic relations, while contrastive terms may contradict them. In this paper, we present a method of taxonomic relation identification that incorporates the trustiness of source texts measured with such techniques as PageRank and knowledge-based trust, and the collective evidence of synonyms and contrastive terms identified by linguistic pattern matching and machine learning. The experimental results show that the proposed features can consistently improve performance up to 4%-10% of F-measure.

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“…The task is also known as Entity Linking or Entity Resolution (Bunescu and Pasca, 2006;McNamee and Dang, 2009;Hachey et al, 2012). NED is confounded by the ambiguity of named entity mentions.…”

Section: Introductionmentioning

confidence: 99%

“…For a given mention in context, NED systems (Hachey et al, 2012;Lazic et al, 2015) typically rely on two models: (1) a mention module returns possible entities which can be referred to by the mention, ordered by prior probabilities; (2) a con- Figure 1: Two examples where NED systems fail, motivating our two background models: similar entities (top) and selectional preferences (bottom). The logos correspond to the gold label.…”

Section: Introductionmentioning

confidence: 99%

Alleviating Poor Context with Background Knowledge for Named Entity Disambiguation

Barrena¹,

Soroa²,

Agirre³

2016

Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Named Entity Disambiguation (NED) algorithms disambiguate mentions of named entities with respect to a knowledge-base, but sometimes the context might be poor or misleading. In this paper we introduce the acquisition of two kinds of background information to alleviate that problem: entity similarity and selectional preferences for syntactic positions. We show, using a generative Näive Bayes model for NED, that the additional sources of context are complementary, and improve results in the CoNLL 2003 and TAC KBP DEL 2014 datasets, yielding the third best and the best results, respectively. We provide examples and analysis which show the value of the acquired background information.

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Evaluating Entity Linking with Wikipedia

Cited by 211 publications

References 28 publications

A Latent Variable Model for Discourse-aware Concept and Entity Disambiguation

A Latent Variable Model for Discourse-aware Concept and Entity Disambiguation

Incorporating Trustiness and Collective Synonym/Contrastive Evidence into Taxonomy Construction

Alleviating Poor Context with Background Knowledge for Named Entity Disambiguation

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