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In natural language processing, entity linking, named entity disambiguation (NED), named entity recognition and disambiguation (NERD) or named entity normalization (NEN)〔 is the task of determining the identity of entities mentioned in text. For example, given the sentence "Paris is the capital of France", the idea is to determine that "Paris" refers to the city of Paris and not to Paris Hilton or any other entity that could be referred as "Paris". NED is different from named entity recognition (NER) in that NER identifies the occurrence or mention of a named entity in text but it does not identify which specific entity it is. Entity linking requires a knowledge base containing the entities to which entity mentions can be linked. A popular choice for entity linking on open domain text are knowledge-bases based on Wikipedia,〔〔Xianpei Han, Le Sun and Jun Zhao (2011). (Collective entity linking in web text: a graph-based method ). Proc. SIGIR.〕 in which each page is regarded as a named entity. NED using Wikipedia entities has been also called wikification (see Wikify! an early entity linking system〔Rada Mihalcea and Andras Csomai (2007)(Wikify! Linking Documents to Encyclopedic Knowledge ). Proc. CIKM.〕 ). A knowledge base may also be induced automatically from training text〔Aaron M. Cohen (2005). Unsupervised gene/protein named entity normalization using automatically extracted dictionaries. Proc. ACL-ISMB Workshop on Linking Biological Literature, Ontologies and Databases: Mining Biological Semantics, pp. 17–24.〕 or manually built.〔Wikidata〕 Named entity mentions can be highly ambiguous, any entity linking method must address this inherent ambiguity. Various approaches to tackle this problem have been tried to date. In the seminal approach of Milne and Witten, supervised learning is employed using the anchor texts of Wikipedia entities as training data.〔David Milne and Ian H. Witten (2008). Learning to link with Wikipedia. Proc. CIKM.〕 Kulkarni ''et al.'' exploited the common property that topically coherent documents refer to entities belonging to strongly related types. Other approaches also collected trainning data based on unambiguous synonyms. More recent systems for NED include AIDA,〔Hoffart, J., Yosef, M. A., Bordino, I., Fürstenau, H., Pinkal, M., Spaniol, M., Taneva, B., Thater, S., and Weikum, G. (2011). (Robust disambiguation of named entities in text ). In EMNLP〕 AGDISTIS〔Usbeck, R., Ngomo, A. N., Röder, M., Gerber, D., Coelho, S. A., Auer, S., and Both, A. (2014). ( AGDISTIS - graph-based disambiguation of named entities using linked data. In ISWC )〕 and Babelfy.〔Moro, A., Raganato, A., and Navigli, R. (2014).(Entity Linking meets Word Sense Dis- ambiguation: a Unified Approach )〕 Entity linking has been used to improve the performance of information retrieval systems〔M. A. Khalid, V. Jijkoun and M. de Rijke (2008). (The impact of named entity normalization on information retrieval for question answering ). Proc. ECIR.〕 and to improve search performance on digital libraries.〔Hui Han, Hongyuan Zha, C. Lee Giles, "Name disambiguation in author citations using a K-way spectral clustering method," ACM/IEEE Joint Conference on Digital Libraries 2005 (JCDL 2005): 334-343, 2005〕〔()〕 NED is also a key input for Semantic Search.〔(STICS )〕 (Entity Linking evaluation campaigns ) are organized by the U.S. National Institute of Standards and Technology (NIST) in the context of the (Knowledge Base Population task ) of the Text Analysis Conference. ==See also== * Explicit semantic analysis * Information extraction * Linked data * Word sense disambiguation * Record linkage 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「entity linking」の詳細全文を読む スポンサード リンク
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