Production of Large Analogical Clusters from Smaller Example Seed Clusters Using Word Embeddings

Yuzhong Hong, Yves Lepage

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

We introduce a method to automatically produce large analogical clusters from smaller seed clusters of representative examples. The method is based on techniques of processing and solving analogical equations in word vector space models, i.e., word embeddings. In our experiments, we use standard data sets in English which cover different relations extending from derivational morphology (like adjective–adverb, positive–comparative forms of adjectives) or inflectional morphology (like present–past forms) to encyclopedic semantics (like country–capital relations). The analogical clusters produced by our method are shown to be of reasonably good quality, as shown by comparing human judgment against automatic NDCG@n scores. In total, they contain 8.5 times as many relevant word pairs as the seed clusters.

本文言語English
ホスト出版物のタイトルCase-Based Reasoning Research and Development - 26th International Conference, ICCBR 2018, Proceedings
編集者Michael T. Cox, Peter Funk, Shahina Begum
出版社Springer Verlag
ページ548-562
ページ数15
ISBN(印刷版)9783030010805
DOI
出版ステータスPublished - 2018 1 1
イベント26th International Conference on Case-Based Reasoning, ICCBR 2018 - Stockholm, Sweden
継続期間: 2018 7 92018 7 12

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
11156 LNAI
ISSN(印刷版)0302-9743
ISSN(電子版)1611-3349

Other

Other26th International Conference on Case-Based Reasoning, ICCBR 2018
CountrySweden
CityStockholm
Period18/7/918/7/12

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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