4 citations · 7 across the 3 of their papers we have counts for
7 papers
A Structured Distributional Model of Sentence Meaning and Processing
Emmanuele Chersoni, Enrico Santus, Ludovica Pannitto +3
Most compositional distributional semantic models represent sentence meaning with a single vector. In this paper, we propose a Structured Distributional Model (SDM) that combines w…
Is Structure Necessary for Modeling Argument Expectations in Distributional Semantics?
Emmanuele Chersoni, Enrico Santus, Philippe Blache +1
Despite the number of NLP studies dedicated to thematic fit estimation, little attention has been paid to the related task of composing and updating verb argument expectations. The…
Measuring Thematic Fit with Distributional Feature Overlap
Enrico Santus, Emmanuele Chersoni, Alessandro Lenci +1
In this paper, we introduce a new distributional method for modeling predicate-argument thematic fit judgments. We use a syntax-based DSM to build a prototypical representation of…
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…