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of 36
pro vyhledávání: '"Herbelot, Aurélie"'
Interpretability methods in NLP aim to provide insights into the semantics underlying specific system architectures. Focusing on word embeddings, we present a supervised-learning method that, for a given domain (e.g., sports, professions), identifies
Externí odkaz:
http://arxiv.org/abs/2310.10262
Autor:
Pannitto, Ludovica, Herbelot, Aurélie
Publikováno v:
Proceedings of the First International Workshop on Construction Grammars and NLP (CxGs+NLP, GURT/SyntaxFest 2023)
This paper presents a novel framework for evaluating Neural Language Models' linguistic abilities using a constructionist approach. Not only is the usage-based model in line with the underlying stochastic philosophy of neural architectures, but it al
Externí odkaz:
http://arxiv.org/abs/2302.03589
Autor:
Bruera, Andrea, Herbelot, Aurélie
In human semantic cognition, proper names (names which refer to individual entities) are harder to learn and retrieve than common nouns. This seems to be the case for machine learning algorithms too, but the linguistic and distributional reasons for
Externí odkaz:
http://arxiv.org/abs/2104.10270
Autor:
Pannitto, Ludovica, Herbelot, Aurélie
Recurrent Neural Networks (RNNs) have been shown to capture various aspects of syntax from raw linguistic input. In most previous experiments, however, learning happens over unrealistic corpora, which do not reflect the type and amount of data a chil
Externí odkaz:
http://arxiv.org/abs/2010.04637
Autor:
Erk, Katrin, Herbelot, Aurelie
In this paper, we derive a notion of 'word meaning in context' that characterizes meaning as both intensional and conceptual. We introduce a framework for specifying local as well as global constraints on word meaning in context, together with their
Externí odkaz:
http://arxiv.org/abs/2009.07936
Autor:
Herbelot, Aurelie, Baroni, Marco
Distributional semantics models are known to struggle with small data. It is generally accepted that in order to learn 'a good vector' for a word, a model must have sufficient examples of its usage. This contradicts the fact that humans can guess the
Externí odkaz:
http://arxiv.org/abs/1707.06556
Autor:
Shekhar, Ravi, Pezzelle, Sandro, Klimovich, Yauhen, Herbelot, Aurelie, Nabi, Moin, Sangineto, Enver, Bernardi, Raffaella
In this paper, we aim to understand whether current language and vision (LaVi) models truly grasp the interaction between the two modalities. To this end, we propose an extension of the MSCOCO dataset, FOIL-COCO, which associates images with both cor
Externí odkaz:
http://arxiv.org/abs/1705.01359
Autor:
Sorodoc, Ionut, Pezzelle, Sandro, Herbelot, Aurélie, Dimiccoli, Mariella, Bernardi, Raffaella
Major advances have recently been made in merging language and vision representations. But most tasks considered so far have confined themselves to the processing of objects and lexicalised relations amongst objects (content words). We know, however,
Externí odkaz:
http://arxiv.org/abs/1704.02923
Akademický článek
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Autor:
Erk, Katrin, Herbelot, Aurélie
Publikováno v:
Journal of Semantics; Nov2023, Vol. 40 Issue 4, p549-583, 35p
