ROOT13: Spotting Hypernyms, Co-Hyponyms and Randoms
DOI:
https://doi.org/10.1609/aaai.v30i1.9931Keywords:
Semantic Relations, Semantics, Hypernymy, Entailment, Classifier, Featurese, Unsupervised, Vector Space Models, VSMs, Distributional Semantic Models, DSMsAbstract
In this paper, we describe ROOT13, a supervised system for the classification of hypernyms, co-hyponyms and random words. The system relies on a Random Forest algorithm and 13 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, ROOT13 achieves an F1 score of 88.3%, against a baseline of 57.6% (vector cosine). When the classification is binary, ROOT13 achieves the following results: hypernyms-co-hyponyms (93.4% vs. 60.2%), hypernyms-random (92.3% vs. 65.5%) and co-hyponyms-random (97.3% vs. 81.5%). Our results are competitive with state-of-the-art models.
