Collective Entity Disambiguation with Structured Gradient Tree Boosting

Yang, Yi; İrsoy, Ozan; Rahman, Kazi Shefaet

doi:10.18653/v1/n18-1071

Cited by 31 publications

(40 citation statements)

References 30 publications

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“…Accuracy Chisholm and Hachey (2015) 88.7 Guo and Barbosa (2018) 89.0 Globerson et al (2016) 91.0 Yamada et al (2016) 91.5 Ganea and Hofmann (2017) 92.22 ± 0.14 Yang et al (2018) 93.0 Le and Titov (2018) 93.07 ± 0.27 Our 94.0 ± 0.28 Our (+pseudo entities)…”

Section: Methodsmentioning

confidence: 99%

Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation

Yamada¹,

Shindo

Takeda

et al. 2016

Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning

300

457

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Named Entity Disambiguation (NED) refers to the task of resolving multiple named entity mentions in a document to their correct references in a knowledge base (KB) (e.g., Wikipedia). In this paper, we propose a novel embedding method specifically designed for NED. The proposed method jointly maps words and entities into the same continuous vector space. We extend the skip-gram model by using two models. The KB graph model learns the relatedness of entities using the link structure of the KB, whereas the anchor context model aims to align vectors such that similar words and entities occur close to one another in the vector space by leveraging KB anchors and their context words. By combining contexts based on the proposed embedding with standard NED features, we achieved state-of-theart accuracy of 93.1% on the standard CoNLL dataset and 85.2% on the TAC 2010 dataset.

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Section: Methodsmentioning

confidence: 99%

Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation

Yamada¹,

Shindo

Takeda

et al. 2016

Proceedings of the 20th SIGNLL Conference on Computational Natural Language Learning

300

457

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“…In the context of ED, recent neural methods He et al (2013); Sun et al (2015); Yamada et al (2016); Ganea and Hofmann (2017); Le and Titov (2018); Yang et al (2018); Radhakrishnan et al (2018) have established state-of-the-art results, outperforming engineered features based models. Context aware word, span and entity embeddings, together with neural similarity functions, are essential in these frameworks.…”

Section: Related Workmentioning

confidence: 99%

End-to-End Neural Entity Linking

Kolitsas¹,

Ganea

Hofmann

2018

Proceedings of the 22nd Conference on Computational Natural Language Learning

211

269

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Entity Linking (EL) is an essential task for semantic text understanding and information extraction. Popular methods separately address the Mention Detection (MD) and Entity Disambiguation (ED) stages of EL, without leveraging their mutual dependency. We here propose the first neural end-to-end EL system that jointly discovers and links entities in a text document. The main idea is to consider all possible spans as potential mentions and learn contextual similarity scores over their entity candidates that are useful for both MD and ED decisions. Key components are context-aware mention embeddings, entity embeddings and a probabilistic mention -entity map, without demanding other engineered features. Empirically, we show that our end-to-end method significantly outperforms popular systems on the Gerbil platform when enough training data is available. Conversely, if testing datasets follow different annotation conventions compared to the training set (e.g. queries/ tweets vs news documents), our ED model coupled with a traditional NER system offers the best or second best EL accuracy.

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“…The best model for one dataset may perform poorly on others. An example is the SGTB-BiBSG model [99], which performed well on the WNED-CWEB dataset but not on the others. Only a small number of models performed best on more than one dataset.…”

Section: ) Disambiguation-only Nel Methodsmentioning

confidence: 99%

Named Entity Extraction for Knowledge Graphs: A Literature Overview

et al. 2020

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An enormous amount of digital information is expressed as natural-language (NL) text that is not easily processable by computers. Knowledge Graphs (KG) offer a widely used format for representing information in computer-processable form. Natural Language Processing (NLP) is therefore needed for mining (or lifting) knowledge graphs from NL texts. A central part of the problem is to extract the named entities in the text. The paper presents an overview of recent advances in this area, covering: Named Entity Recognition (NER), Named Entity Disambiguation (NED), and Named Entity Linking (NEL). We comment that many approaches to NED and NEL are based on older approaches to NER and need to leverage the outputs of state-of-the-art NER systems. There is also a need for standard methods to evaluate and compare named-entity extraction approaches. We observe that NEL has recently moved from being stepwise and isolated into an integrated process along two dimensions: the first is that previously sequential steps are now being integrated into end-to-end processes, and the second is that entities that were previously analysed in isolation are now being lifted in each other's context. The current culmination of these trends are the deep-learning approaches that have recently reported promising results.

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Collective Entity Disambiguation with Structured Gradient Tree Boosting

Cited by 31 publications

References 30 publications

Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation

Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation

End-to-End Neural Entity Linking

Named Entity Extraction for Knowledge Graphs: A Literature Overview

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