Vector Embedding of Wikipedia Concepts and Entities
Authors | Ehsan Sherkat Evangelos E. Milios |
|---|---|
Publication date | 2017 |
DOI | 10.1007/978-3-319-59569-6_50 |
Links | Original Preprint |
Vector Embedding of Wikipedia Concepts and Entities - scientific work related to Wikipedia quality published in 2017, written by Ehsan Sherkat and Evangelos E. Milios.
Overview
Using deep learning for different machine learning tasks such as word embedding has recently gained a lot of researchers’ attention. Word embedding is the task of mapping words or phrases to a low dimensional numerical vector. In this paper, authors use deep learning to embed Wikipedia concepts and entities. The English version of Wikipedia contains more than five million pages, which suggest its capability to cover many English entities, phrases, and concepts. Each Wikipedia page is considered as a concept. Some concepts correspond to entities, such as a person’s name, an organization or a place. Contrary to word embedding, Wikipedia concepts embedding is not ambiguous, so there are different vectors for concepts with similar surface form but different mentions. Authors proposed several approaches and evaluated their performance based on Concept Analogy and Concept Similarity tasks. The results show that proposed approaches have the performance comparable and in some cases even higher than the state-of-the-art methods.
Embed
Wikipedia Quality
Sherkat, Ehsan; Milios, Evangelos E.. (2017). "[[Vector Embedding of Wikipedia Concepts and Entities]]". Springer, Cham. DOI: 10.1007/978-3-319-59569-6_50.
English Wikipedia
{{cite journal |last1=Sherkat |first1=Ehsan |last2=Milios |first2=Evangelos E. |title=Vector Embedding of Wikipedia Concepts and Entities |date=2017 |doi=10.1007/978-3-319-59569-6_50 |url=https://wikipediaquality.com/wiki/Vector_Embedding_of_Wikipedia_Concepts_and_Entities |journal=Springer, Cham}}
HTML
Sherkat, Ehsan; Milios, Evangelos E.. (2017). "<a href="https://wikipediaquality.com/wiki/Vector_Embedding_of_Wikipedia_Concepts_and_Entities">Vector Embedding of Wikipedia Concepts and Entities</a>". Springer, Cham. DOI: 10.1007/978-3-319-59569-6_50.
