ROOT13: Spotting hypernyms, co-hyponyms and randoms

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

2 Citations (Scopus)


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%), hypernymsrandom (92.3% vs. 65.5%) and co-hyponyms-random (97.3% vs. 81.5%). Our results are competitive with stateof-The-Art models.
Original languageEnglish
Title of host publication30th AAAI Conference on Artificial Intelligence, AAAI 2016
PublisherAAAI press
Number of pages2
ISBN (Electronic)9781577357605
Publication statusPublished - 1 Jan 2016
Event30th AAAI Conference on Artificial Intelligence, AAAI 2016 - Phoenix Convention Center, Phoenix, United States
Duration: 12 Feb 201617 Feb 2016


Conference30th AAAI Conference on Artificial Intelligence, AAAI 2016
Country/TerritoryUnited States

ASJC Scopus subject areas

  • Artificial Intelligence


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