M
Marco Baroni
Researcher at Facebook
Publications - 232
Citations - 17918
Marco Baroni is an academic researcher from Facebook. The author has contributed to research in topics: Distributional semantics & Semantic similarity. The author has an hindex of 58, co-authored 227 publications receiving 15594 citations. Previous affiliations of Marco Baroni include Austrian Research Institute for Artificial Intelligence & Catalan Institution for Research and Advanced Studies.
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Proceedings ArticleDOI
Don't count, predict! A systematic comparison of context-counting vs. context-predicting semantic vectors
TL;DR: An extensive evaluation of context-predicting models with classic, count-vector-based distributional semantic approaches, on a wide range of lexical semantics tasks and across many parameter settings shows that the buzz around these models is fully justified.
Journal ArticleDOI
The WaCky wide web: a collection of very large linguistically processed web-crawled corpora
TL;DR: UkWaC, deWaC and itWaC are introduced, three very large corpora of English, German, and Italian built by web crawling, and the methodology and tools used in their construction are described.
Journal ArticleDOI
Multimodal distributional semantics
TL;DR: This work proposes a flexible architecture to integrate text- and image-based distributional information, and shows in a set of empirical tests that the integrated model is superior to the purely text-based approach, and it provides somewhat complementary semantic information with respect to the latter.
Proceedings Article
A SICK cure for the evaluation of compositional distributional semantic models
Marco Marelli,Stefano Menini,Marco Baroni,Luisa Bentivogli,Raffaella Bernardi,Roberto Zamparelli +5 more
TL;DR: This work aims to help the research community working on compositional distributional semantic models (CDSMs) by providing SICK (Sentences Involving Compositional Knowldedge), a large size English benchmark tailored for them.
Journal ArticleDOI
Distributional memory: A general framework for corpus-based semantics
Marco Baroni,Alessandro Lenci +1 more
TL;DR: The Distributional Memory approach is shown to be tenable despite the constraints imposed by its multi-purpose nature, and performs competitively against task-specific algorithms recently reported in the literature for the same tasks, and against several state-of-the-art methods.