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  1.  18
    Mechanisms for handling nested dependencies in neural-network language models and humans.Yair Lakretz, Dieuwke Hupkes, Alessandra Vergallito, Marco Marelli, Marco Baroni & Stanislas Dehaene - 2021 - Cognition 213 (C):104699.
  2.  34
    Multimodal Word Meaning Induction From Minimal Exposure to Natural Text.Angeliki Lazaridou, Marco Marelli & Marco Baroni - 2017 - Cognitive Science 41 (S4):677-705.
    By the time they reach early adulthood, English speakers are familiar with the meaning of thousands of words. In the last decades, computational simulations known as distributional semantic models have demonstrated that it is possible to induce word meaning representations solely from word co-occurrence statistics extracted from a large amount of text. However, while these models learn in batch mode from large corpora, human word learning proceeds incrementally after minimal exposure to new words. In this study, we run a set (...)
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  3.  24
    Affixation in semantic space: Modeling morpheme meanings with compositional distributional semantics.Marco Marelli & Marco Baroni - 2015 - Psychological Review 122 (3):485-515.
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  4.  8
    Strudel: A Corpus‐Based Semantic Model Based on Properties and Types.Marco Baroni, Eduard Barbu, Brian Murphy & Massimo Poesio - 2010 - Cognitive Science 34 (2):222-254.
    Computational models of meaning trained on naturally occurring text successfully model human performance on tasks involving simple similarity measures, but they characterize meaning in terms of undifferentiated bags of words or topical dimensions. This has led some to question their psychological plausibility (Murphy, 2002;Schunn, 1999). We present here a fully automatic method for extracting a structured and comprehensive set of concept descriptions directly from an English part‐of‐speech‐tagged corpus. Concepts are characterized by weighted properties, enriched with concept–property types that approximate classical (...)
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  5.  46
    Strudel: A Corpus‐Based Semantic Model Based on Properties and Types.Marco Baroni, Brian Murphy, Eduard Barbu & Massimo Poesio - 2010 - Cognitive Science 34 (2):222-254.
    Computational models of meaning trained on naturally occurring text successfully model human performance on tasks involving simple similarity measures, but they characterize meaning in terms of undifferentiated bags of words or topical dimensions. This has led some to question their psychological plausibility (Murphy, 2002;Schunn, 1999). We present here a fully automatic method for extracting a structured and comprehensive set of concept descriptions directly from an English part‐of‐speech‐tagged corpus. Concepts are characterized by weighted properties, enriched with concept–property types that approximate classical (...)
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  6.  8
    Spicy Adjectives and Nominal Donkeys: Capturing Semantic Deviance Using Compositionality in Distributional Spaces.Eva M. Vecchi, Marco Marelli, Roberto Zamparelli & Marco Baroni - 2017 - Cognitive Science 41 (1):102-136.
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  7.  24
    Spicy Adjectives and Nominal Donkeys: Capturing Semantic Deviance Using Compositionality in Distributional Spaces.Eva M. Vecchi, Marco Marelli, Roberto Zamparelli & Marco Baroni - 2016 - Cognitive Science 40 (7):102-136.
    Sophisticated senator and legislative onion. Whether or not you have ever heard of these things, we all have some intuition that one of them makes much less sense than the other. In this paper, we introduce a large dataset of human judgments about novel adjective-noun phrases. We use these data to test an approach to semantic deviance based on phrase representations derived with compositional distributional semantic methods, that is, methods that derive word meanings from contextual information, and approximate phrase meanings (...)
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