ROOT13: Spotting hypernyms, co-hyponyms and randoms

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

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 proceeding › Conference article published in proceeding or book › Academic research › peer-review

2 Citations (Scopus)

Abstract

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 language	English
Title of host publication	30th AAAI Conference on Artificial Intelligence, AAAI 2016
Publisher	AAAI press
Pages	4262-4263
Number of pages	2
ISBN (Electronic)	9781577357605
Publication status	Published - 1 Jan 2016
Event	30th AAAI Conference on Artificial Intelligence, AAAI 2016 - Phoenix Convention Center, Phoenix, United States Duration: 12 Feb 2016 → 17 Feb 2016

Conference

Conference	30th AAAI Conference on Artificial Intelligence, AAAI 2016
Country/Territory	United States
City	Phoenix
Period	12/02/16 → 17/02/16

ASJC Scopus subject areas

Artificial Intelligence

Cite this

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title = "ROOT13: Spotting hypernyms, co-hyponyms and randoms",

abstract = "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.",

author = "Enrico Santus and Alessandro Lenci and Chiu, {Tin Shing} and Qin Lu and Chu-ren Huang",

year = "2016",

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day = "1",

language = "English",

pages = "4262--4263",

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note = "30th AAAI Conference on Artificial Intelligence, AAAI 2016 ; Conference date: 12-02-2016 Through 17-02-2016",

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T1 - ROOT13: Spotting hypernyms, co-hyponyms and randoms

AU - Santus, Enrico

AU - Lenci, Alessandro

AU - Chiu, Tin Shing

AU - Lu, Qin

AU - Huang, Chu-ren

PY - 2016/1/1

Y1 - 2016/1/1

N2 - 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.

AB - 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.

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M3 - Conference article published in proceeding or book

SP - 4262

EP - 4263

BT - 30th AAAI Conference on Artificial Intelligence, AAAI 2016

PB - AAAI press

T2 - 30th AAAI Conference on Artificial Intelligence, AAAI 2016

Y2 - 12 February 2016 through 17 February 2016

ER -

ROOT13: Spotting hypernyms, co-hyponyms and randoms

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ASJC Scopus subject areas

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