Evaluating semantic relatedness through categorical and contextual information for entity disambiguation

Yiming Zhang, Mizuho Iwaihara

研究成果: Conference contribution

2 被引用数 (Scopus)

抄録

The number of entities in large-scale knowledge bases has been growing in recent years. The key issue to entity linking using a knowledge base such as Wikipedia is entity disambiguation. The objective of our proposing system is to disambiguate entities in documents and link entity mentions to their corresponding Wikipedia articles. To this end, our system ranks the set of candidate entities based on relatedness by utilizing semantic features derived from Wikipedia category hierarchies and articles. In addition, to reflect contextual information of Wikipedia, we utilize word embedding for refining the ranking result of candidate entities. Our experimental results show that these features have given good correlation with human rankings in candidate relatedness ranking and the combination of features has high disambiguation accuracy on news articles.

本文言語English
ホスト出版物のタイトル2016 IEEE/ACIS 15th International Conference on Computer and Information Science, ICIS 2016 - Proceedings
編集者Kuniaki Uehara, Masahide Nakamura
出版社Institute of Electrical and Electronics Engineers Inc.
ISBN(電子版)9781509008063
DOI
出版ステータスPublished - 2016 8 23
イベント15th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2016 - Okayama, Japan
継続期間: 2016 6 262016 6 29

出版物シリーズ

名前2016 IEEE/ACIS 15th International Conference on Computer and Information Science, ICIS 2016 - Proceedings

Other

Other15th IEEE/ACIS International Conference on Computer and Information Science, ICIS 2016
国/地域Japan
CityOkayama
Period16/6/2616/6/29

ASJC Scopus subject areas

  • コンピュータ サイエンス(全般)
  • エネルギー工学および電力技術
  • 制御と最適化

フィンガープリント

「Evaluating semantic relatedness through categorical and contextual information for entity disambiguation」の研究トピックを掘り下げます。これらがまとまってユニークなフィンガープリントを構成します。

引用スタイル