Rubenstein-Goodenough (RG-65) Dataset

A Rubenstein-Goodenough (RG-65) Dataset is a Semantic Word Similarity Dataset that can be used in a Semantic Word Similarity Benchmark Task.

• AKA: RG-65 Dataset.
• Context:
  • It was first introduced by Rubenstein & Goodenough (1965).
• Example(s):
  • https://www.wikidata.org/wiki/Q43981247
  • http://lcl.uniroma1.it/similarity-datasets/
  • …
• Counter-Example(s):
  • SimLex-999 Dataset,
  • MC30 Dataset,
  • WordSim-353 Dataset.
• See: Training Dataset, Semantic Word Similarity Measure, Semantic Word Similarity System, SemEval-2017 Task 2, Reading Comprehension Dataset, Question-Answer Dataset.

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References

2019

• (ACL, 2019) ⇒ https://aclweb.org/aclwiki/RG-65_Test_Collection_(State_of_the_art) Last updated: 15 September 2019.
  • QUOTE: state of the art in Rubenstein & Goodenough (RG-65) dataset 65 word pairs;
    • Similarity of each pair is scored according to a scale from 0 to 4 (the higher the “similarity of meaning", the higher the number);
    • The similarity values in the dataset are the means of judgments made by 51 subjects (Rubenstein and Goodenough, 1965).

2017

• (Camacho-Collados et al., 2017) ⇒ Jose Camacho-Collados, Mohammad Taher Pilehvar, Nigel Collier, and Roberto Navigli. (2017). “SemEval-2017 Task 2: Multilingual and Cross-lingual Semantic Word Similarity.” In: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval@ACL 2017).

2015

• (Camacho-Collados et al., 2015) ⇒ Jose Camacho-Collados, Mohammad Taher Pilehvar, and Roberto Navigli. (2015). “A Framework for the Construction of Monolingual and Cross-lingual Word Similarity Datasets.” In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing (ACL 2015) Volume 2: Short Papers.

1965

• (Rubenstein & Goodenough, 1965) ⇒ Herbert Rubenstein, and John B. Goodenough (1965). "Contextual Correlates of Synonymy". In: Communications of the ACM (CACM) 8 (10).

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