What a Nerd! Beating students and Vector Cosine in the ESL and TOEFL datasets

Enrico Santus, Tin Shing Chiu, Qin Lu, Alessandro Lenci, Chu-ren Huang

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

5 Citations (Scopus)


In this paper, we claim that Vector Cosine - which is generally considered one of the most efficient unsupervised measures for identifying word similarity in Vector Space Models - can be outperformed by a completely unsupervised measure that evaluates the extent of the intersection among the most associated contexts of two target words, weighting such intersection according to the rank of the shared contexts in the dependency ranked lists. This claim comes from the hypothesis that similar words do not simply occur in similar contexts, but they share a larger portion of their most relevant contexts compared to other related words. To prove it, we describe and evaluate APSyn, a variant of Average Precision that - independently of the adopted parameters - outperforms the Vector Cosine and the co-occurrence on the ESL and TOEFL test sets. In the best setting, APSyn reaches 0.73 accuracy on the ESL dataset and 0.70 accuracy in the TOEFL dataset, beating therefore the non-English US college applicants (whose average, as reported in the literature, is 64.50%) and several state-of-the-art approaches.
Original languageEnglish
Title of host publicationProceedings of the 10th International Conference on Language Resources and Evaluation, LREC 2016
PublisherEuropean Language Resources Association (ELRA)
Number of pages8
ISBN (Electronic)9782951740891
Publication statusPublished - 1 Jan 2016
Event10th International Conference on Language Resources and Evaluation, LREC 2016 - Grand Hotel Bernardin Conference Center, Portoroz, Slovenia
Duration: 23 May 201628 May 2016


Conference10th International Conference on Language Resources and Evaluation, LREC 2016


  • Distributional semantic models
  • DSMs
  • Semantic relations
  • Vector Space Models
  • VSMs
  • Words similarity

ASJC Scopus subject areas

  • Linguistics and Language
  • Library and Information Sciences
  • Language and Linguistics
  • Education

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