Author:
Fernandez Garcia N.,Arias Fisteus J.,Sanchez Fernandez L.
Abstract
In recent years, the task of automatically linking pieces of text (anchors) mentioned in a document to Wikipedia articles that represent the meaning of these anchors has received extensive research attention. Typically, link-to-Wikipedia systems try to find a set of Wikipedia articles that are candidates to represent the meaning of the anchor and, later, rank these candidates to select the most appropriate one. In this ranking process the systems rely on context information obtained from the document where the anchor is mentioned and/or from Wikipedia. In this paper we center our attention in the use of Wikipedia links as context information. In particular, we offer a review of several candidate ranking approaches in the state-of-the-art that rely on Wikipedia link information. In addition, we provide a comparative empirical evaluation of the different approaches on five different corpora: the TAC 2010 corpus and four corpora built from actual Wikipedia articles and news items.
Cited by
8 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
1. Identifying Salient Entities of News Articles Using Binary Salient Classifier;2021 IEEE International Conference on Big Data (Big Data);2021-12-15
2. English teaching practice based on artificial intelligence technology;Journal of Intelligent & Fuzzy Systems;2019-10-09
3. Exploiting semantic similarity for named entity disambiguation in knowledge graphs;Expert Systems with Applications;2018-07
4. Patterns for Distributed Real-Time Stream Processing;IEEE Transactions on Parallel and Distributed Systems;2017-11-01
5. Lightweight Multilingual Entity Extraction and Linking;Proceedings of the Tenth ACM International Conference on Web Search and Data Mining;2017-02-02