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

Yuzhong Hong*, Yves Lepage

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationCase-Based Reasoning Research and Development - 26th International Conference, ICCBR 2018, Proceedings
EditorsMichael T. Cox, Peter Funk, Shahina Begum
PublisherSpringer Verlag
Number of pages15
ISBN (Print)9783030010805
Publication statusPublished - 2018
Event26th International Conference on Case-Based Reasoning, ICCBR 2018 - Stockholm, Sweden
Duration: 2018 Jul 92018 Jul 12

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11156 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other26th International Conference on Case-Based Reasoning, ICCBR 2018


  • Analogical clusters
  • Analogy
  • Word embeddings

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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