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Found 5 papers, 1 papers with code
Unsupervised Measure of Word Similarity: How to Outperform Co-occurrence and Vector Cosine in VSMs
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 be outperformed by an unsupervised measure that calculates the extent of the intersection among the most mutually dependent contexts of the target words.
Nine Features in a Random Forest to Learn Taxonomical Semantic Relations
When the classification is binary, ROOT9 achieves the following results against the baseline: hypernyms-co-hyponyms 95. 7% vs. 69. 8%, hypernyms-random 91. 8% vs. 64. 1% and co-hyponyms-random 97. 8% vs. 79. 4%.
What a Nerd! Beating Students and Vector Cosine in the ESL and TOEFL Datasets
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, can be outperformed by a completely unsupervised measure that evaluates the extent of the intersection among the most associated contexts of two target words, weighting such intersection according to the rank of the shared contexts in the dependency ranked lists.
ROOT13: Spotting Hypernyms, Co-Hyponyms and Randoms
In this paper, we describe ROOT13, a supervised system for the classification of hypernyms, co-hyponyms and random words.
A Grammar-informed Corpus-based Sentence Database for Linguistic and Computational Studies
We adopt the corpus-informed approach to example sentence selections for the construction of a reference grammar.
