Nine Features in a Random Forest to Learn Taxonomical Semantic Relations

@article{Santus2016NineFI,
  title={Nine Features in a Random Forest to Learn Taxonomical Semantic Relations},
  author={Enrico Santus and Alessandro Lenci and Tin-Shing Chiu and Qin Lu and Chu-Ren Huang},
  journal={CoRR},
  year={2016},
  volume={abs/1603.08702}
}
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… CONTINUE READING
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  • When all the classes are present, ROOT9 achieves an F1 score of 90.7%, against a baseline of 57.2% (vector cosine).

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