Results 11 to 20 of about 14,087 (197)
Named Entity Disambiguation for Noisy Text [PDF]
Proceedings of the 21st Conference on Computational Natural Language
Learning (CoNLL 2017), 2017 We address the task of Named Entity Disambiguation (NED) for noisy text. We present WikilinksNED, a large-scale NED dataset of text fragments from the web, which is significantly noisier and more challenging than existing news-based datasets. To capture the limited and noisy local context surrounding each mention, we design a neural model and train it ...
Eshel, Yotam +5 more
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Domain-specific Named Entity Disambiguation in Historical Memoirs [PDF]
, 2017 This paper presents the results of the extraction of named entities from a collection of historical memoirs about the italian Resistance during the World War II. The methodology followed for the extraction and disambiguation task will be discussed, as well as its evaluation.
Marco Rovera +3 more
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Named Entity Extraction for Knowledge Graphs: A Literature Overview
IEEE Access, 2020 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
Tareq Al-Moslmi +3 more
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IEEE Access, 2020 Named Entity Disambiguation (NED) refers to the task of resolving multiple named entity mentions in an input-text sequence to their correct references in a knowledge graph.
Zizheng Ji +3 more
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Towards zero-shot cross-lingual named entity disambiguation
Expert Systems with Applications, 2021 [EN]In cross-Lingual Named Entity Disambiguation (XNED) the task is to link Named Entity mentions in text in some native language to English entities in a knowledge graph. XNED systems usually require training data for each native language, limiting their application for low resource languages with small amounts of training data.
Barrena Madinabeitia, Ander +2 more
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An Approach to Web-Scale Named-Entity Disambiguation [PDF]
, 2009 We present a multi-pass clustering approach to large scale. wide-scope named-entity disambiguation (NED) oil collections of web pages. Our approach Uses name co-occurrence information to cluster and hence disambiguate entities. and is designed to handle NED on the entire web.
Luís Sarmento +3 more
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Improving named entity disambiguation by iteratively enhancing certainty of extraction [PDF]
, 2011 Named entity extraction and disambiguation have received much attention in recent years. Typical fields addressing these topics are information retrieval, natural language processing, and semantic web.
Habib, Mena B., Keulen, Maurice van
core +17 more sources
Named entity extraction and disambiguation [PDF]
Proceedings of the sixth international workshop on Exploiting semantic annotations in information retrieval, 2013 Named entity extraction (NEE) and disambiguation (NED) are two areas of research that are well covered in literature. Typical fields addressing these topics are information retrieval, natural language processing, and semantic web. Although these topics are highly dependent, almost no existing works examine this dependency.
Habib, Mena Badieh, van Keulen, Maurice
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Journal of Cheminformatics, 2020 Background Named Entity Linking systems are a powerful aid to the manual curation of digital libraries, which is getting increasingly costly and inefficient due to the information overload.
Pedro Ruas +2 more
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OTNEL: A Distributed Online Deep Learning Semantic Annotation Methodology
Big Data and Cognitive Computing, 2020 Semantic representation of unstructured text is crucial in modern artificial intelligence and information retrieval applications. The semantic information extraction process from an unstructured text fragment to a corresponding representation from a ...
Christos Makris, Michael Angelos Simos
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