Cross-language latent relational search: Mapping knowledge across languages

Nguyen Tuan Duc*, Danushka Bollegala, Mitsuru Ishizuka

*この研究の対応する著者

研究成果: Conference contribution

12 被引用数 (Scopus)

抄録

Latent relational search (LRS) is a novel approach for mapping knowledge across two domains. Given a source domain knowledge concerning the Moon, "The Moon is a satellite of the Earth", one can form a question {(Moon, Earth), (Ganymede, ?)} to query an LRS engine for new knowledge in the target domain concerning the Ganymede. An LRS engine relies on some supporting sentences such as "Ganymede is a natural satellite of Jupiter." to retrieve and rank "Jupiter" as the first answer. This paper proposes cross-language latent relational search (CLRS) to extend the knowledge mapping capability of LRS from cross-domain knowledge mapping to cross-domain and cross-language knowledge mapping. In CLRS, the supporting sentences for the source pair might be in a different language with that of the target pair. We represent the relation between two entities in an entity pair by lexical patterns of the context surrounding the two entities. We then propose a novel hybrid lexical pattern clustering algorithm to capture the semantic similarity between paraphrased lexical patterns across languages. Experiments on Japanese-English datasets show that the proposed method achieves an MRR of 0.579 for CLRS task, which is comparable to the MRR of an existing monolingual LRS engine.

本文言語English
ホスト出版物のタイトルProceedings of the National Conference on Artificial Intelligence
ページ1237-1242
ページ数6
2
出版ステータスPublished - 2011
外部発表はい
イベント25th AAAI Conference on Artificial Intelligence and the 23rd Innovative Applications of Artificial Intelligence Conference, AAAI-11 / IAAI-11 - San Francisco, CA
継続期間: 2011 8 72011 8 11

Other

Other25th AAAI Conference on Artificial Intelligence and the 23rd Innovative Applications of Artificial Intelligence Conference, AAAI-11 / IAAI-11
CitySan Francisco, CA
Period11/8/711/8/11

ASJC Scopus subject areas

  • ソフトウェア
  • 人工知能

フィンガープリント

「Cross-language latent relational search: Mapping knowledge across languages」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