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Learning Functional Distributional Semantics with Visual Data ...
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IAPUCP at SemEval-2021 task 1: Stacking fine-tuned transformers is almost all you need for lexical complexity prediction
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Investigating Cross-Linguistic Adjective Ordering Tendencies with a Latent-Variable Model ...
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Autoencoding Pixies: Amortised Variational Inference with Graph Convolutions for Functional Distributional Semantics ...
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Linguists Who Use Probabilistic Models Love Them: Quantification in Functional Distributional Semantics ...
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Investigating Cross-Linguistic Adjective Ordering Tendencies with a Latent-Variable Model ...
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Investigating Cross-Linguistic Adjective Ordering Tendencies with a Latent-Variable Model
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In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP) (2020)
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Words are vectors, dependencies are matrices: Learning word embeddings from dependency graphs
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Copestake, Ann; Czarnowska, P; Emerson, Guy. - : Association for Computational Linguistics, 2019. : https://aclanthology.org/volumes/W19-04/, 2019. : IWCS 2019 - Proceedings of the 13th International Conference on Computational Semantics - Long Papers, 2019
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Functional Distributional Semantics: Learning Linguistically Informed Representations from a Precisely Annotated Corpus ...
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Emerson, Guy. - : Apollo - University of Cambridge Repository, 2018
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Functional Distributional Semantics: Learning Linguistically Informed Representations from a Precisely Annotated Corpus
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Emerson, Guy. - : University of Cambridge, 2018. : Department of Computer Science and Technology, 2018. : Trinity College, 2018
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Functional Distributional Semantics
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Emerson, Guy; Copestake, Ann. - : The Association for Computational Linguistics, 2016. : Proceedings of the 1st Workshop on Representation Learning for NLP, 2016
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Abstract:
Vector space models have become popular in distributional semantics, despite the challenges they face in capturing various semantic phenomena. We propose a novel probabilistic framework which draws on both formal semantics and recent advances in machine learning. In particular, we separate predicates from the entities they refer to, allowing us to perform Bayesian inference based on logical forms. We describe an implementation of this framework using a combination of Restricted Boltzmann Machines and feedforward neural networks. Finally, we demonstrate the feasibility of this approach by training it on a parsed corpus and evaluating it on established similarity datasets.
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URL: https://doi.org/10.17863/CAM.7097
https://www.repository.cam.ac.uk/handle/1810/261866
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Lacking integrity: HPSG as a morphosyntactic theory
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Emerson, Guy; Copestake, Ann. - : University Library J. C. Senckenberg, 2015. : http://web.stanford.edu/group/cslipublications/cslipublications/HPSG/2015/emerson-copestake.pdf, 2015. : Proceedings of the International Conference on Head-Driven Phrase Structure Grammar, 2015
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Leveraging a semantically annotated corpus to disambiguate prepositional phrase attachment
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Emerson, Guy; Copestake, Ann. - : The Association for Computer Linguistics, 2015. : https://aclanthology.org/volumes/W15-01/, 2015. : IWCS 2015 - Proceedings of the 11th International Conference on Computational Semantics, 2015
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