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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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Abstract:
Distributional Semantic Models (DSMs) construct vector representations of word meanings based on their contexts. Typically, the contexts of a word are defined as its closest neighbours, but they can also be retrieved from its syntactic dependency relations. In this work, we propose a new dependency-based DSM. The novelty of our model lies in associating an independent meaning representation, a matrix, with each dependency-label. This allows it to capture specifics of the relations between words and contexts, leading to good performance on both intrinsic and extrinsic evaluation tasks. In addition to that, our model has an inherent ability to represent dependency chains as products of matrices which provides a straightforward way of handling further contexts of a word.
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URL: https://doi.org/10.17863/CAM.41815
https://www.repository.cam.ac.uk/handle/1810/294710
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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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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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