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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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Abstract:
Across languages, multiple consecutive adjectives modifying a noun (e.g. "the big red dog") follow certain unmarked ordering rules. While explanatory accounts have been put forward, much of the work done in this area has relied primarily on the intuitive judgment of native speakers, rather than on corpus data. We present the first purely corpus-driven model of multi-lingual adjective ordering in the form of a latent-variable model that can accurately order adjectives across 24 different languages, even when the training and testing languages are different. We utilize this novel statistical model to provide strong converging evidence for the existence of universal, cross-linguistic, hierarchical adjective ordering tendencies. ... : 13 pages, 7 tables, 1 figure. To be published in EMNLP 2020 proceedings ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://dx.doi.org/10.48550/arxiv.2010.04755
https://arxiv.org/abs/2010.04755
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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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| 10 |
Investigating Cross-Linguistic Adjective Ordering Tendencies with a Latent-Variable Model ...
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| 11 |
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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| 12 |
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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| 14 |
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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| 16 |
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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| 17 |
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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| 18 |
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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