論文 Relationship Extraction with Weakly Supervised Learning Based on Process-Structure-Property-Performance Reciprocity

Takeshi Onishi ; Takuya Kadohira SAMURAI ORCID (National Institute for Materials ScienceROR) ; Ikumu Watanabe SAMURAI ORCID (National Institute for Materials ScienceROR)

コレクション

引用
Takeshi Onishi, Takuya Kadohira, Ikumu Watanabe. Relationship Extraction with Weakly Supervised Learning Based on Process-Structure-Property-Performance Reciprocity. SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS. 2018, 19 (1), . https://doi.org/10.1080/14686996.2018.1500852
SAMURAI

説明:

(abstract)

We developed a computer aided material design system that extracts relationships from natural language texts in weakly labeled data. We believe that our study makes a significant contribution to the literature because we trained a machine learning model with minimal annotated relation data to extract relationships between scientific concepts from scientific articles.

権利情報:

キーワード: Natural language processing, knowledge extraction, relation extraction, weakly supervised learning, materials informatics

刊行年月日: 2018-12-31

出版者: Informa UK Limited

掲載誌:

  • SCIENCE AND TECHNOLOGY OF ADVANCED MATERIALS (ISSN: 14686996) vol. 19 issue. 1

研究助成金:

原稿種別: 出版者版 (Version of record)

MDR DOI:

公開URL: https://doi.org/10.1080/14686996.2018.1500852

関連資料:

その他の識別子:

連絡先:

更新時刻: 2024-01-05 22:13:14 +0900

MDRでの公開時刻: 2023-02-17 09:32:02 +0900

ファイル名 サイズ
ファイル名 Relation extraction with weakly supervised learning based on process structure property performance reciprocity.pdf (サムネイル)
application/pdf
サイズ 1.46MB 詳細