Nine features in a Random Forest to learn taxonomical semantic relations

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

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

22 Citations (Scopus)


ROOT9 is a supervised system for the classification of hypernyms, co-hyponyms and random words that is derived from the already introduced ROOT13 (Santus et al., 2016). It relies on a Random Forest algorithm and nine unsupervised corpus-based features. We evaluate it with a 10-fold cross validation on 9,600 pairs, equally distributed among the three classes and involving several Parts-Of-Speech (i.e. adjectives, nouns and verbs). When all the classes are present, ROOT9 achieves an F1 score of 90.7%, against a baseline of 57.2% (vector cosine). When the classification is binary, ROOT9 achieves the following results against the baseline: hypernyms-co-hyponyms 95.7% vs. 69.8%, hypernyms-random 91.8% vs. 64.1% and co-hyponyms-random 97.8% vs. 79.4%. In order to compare the performance with the state-of-the-art, we have also evaluated ROOT9 in subsets of the Weeds et al. (2014) datasets, proving that it is in fact competitive. Finally, we investigated whether the system learns the semantic relation or it simply learns the prototypical hypernyms, as claimed by Levy et al. (2015). The second possibility seems to be the most likely, even though ROOT9 can be trained on negative examples (i.e., switched hypernyms) to drastically reduce this bias.
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


  • Co-hyponymy
  • Distributional semantic models
  • DSMs
  • Entailment
  • Hypernymy
  • Hyponymy
  • Semantic relations
  • Taxonomy
  • Vector space models
  • VSMs

ASJC Scopus subject areas

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

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