4 papers
Unsupervised Measure of Word Similarity: How to Outperform Co-occurrence and Vector Cosine in VSMs
Enrico Santus, Tin-Shing Chiu, Qin Lu +2
In this paper, we claim that vector cosine, which is generally considered among the most efficient unsupervised measures for identifying word similarity in Vector Space Models, can…
ROOT13: Spotting Hypernyms, Co-Hyponyms and Randoms
Enrico Santus, Tin-Shing Chiu, Qin Lu +2
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 unsu…
Nine Features in a Random Forest to Learn Taxonomical Semantic Relations
Enrico Santus, Alessandro Lenci, Tin-Shing Chiu +2
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 o…
What a Nerd! Beating Students and Vector Cosine in the ESL and TOEFL Datasets
Enrico Santus, Tin-Shing Chiu, Qin Lu +2
In this paper, we claim that Vector Cosine, which is generally considered one of the most efficient unsupervised measures for identifying word similarity in Vector Space Models, ca…