Named Entity Disambiguation (NED) System

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A Named Entity Disambiguation (NED) System is a text processing system that can solve an NED task by implementing an NED algorithm.



References

2019

  • (Wikipedia, 2019) ⇒ https://en.wikipedia.org/wiki/Entity_linking Retrieved:2019-6-16.
    • In natural language processing, entity linking, named entity linking (NEL), named entity disambiguation (NED), named entity recognition and disambiguation (NERD) or named entity normalization (NEN) [1] 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, [2] 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[3] [4] or manually built.

      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. [5] Other approaches also collected training data based on unambiguous synonyms. . Kulkarni et al. exploited the common property that topically coherent documents refer to entities belonging to strongly related types.

      Entity linking has been used to improve the performance of information retrieval systems and to improve search performance on digital libraries. [6] [7] NED is also a key input for Semantic Search. [8]

  1. M. A. Khalid, V. Jijkoun and M. de Rijke (2008). The impact of named entity normalization on information retrieval for question answering. Proc. ECIR.
  2. (Han et al., 2011) ⇒ Xianpei Han, Le Sun, and Jun Zhao. (2011). “Collective Entity Linking in Web Text: A Graph-based Method.” In: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval. doi:10.1145/2009916.2010019
  3. (Mihalcea & Csomai, 2007) ⇒ Rada Mihalcea, and Andras Csomai. (2007). “Wikify!: Linking documents to encyclopedic knowledge.” In: Proceedings of the Sixteenth ACM Conference on Information and Knowledge Management (CIKM 2007). doi:10.1145/1321440.1321475]
  4. 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.
  5. (Milne & Witten, 2008a) ⇒ David N. Milne, and Ian H. Witten. (2008). “Learning to Link with Wikipedia.” In: Proceeding of the 17th ACM Conference on Information and Knowledge Management, (CIKM 2008). doi:10.1145/1458082.1458150
  6. 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
  7. [1]
  8. STICS

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