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1
Shapley Idioms: Analysing BERT Sentence Embeddings for General Idiom Token Identification
In: Front Artif Intell (2022)
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2
Poisoning Knowledge Graph Embeddings via Relation Inference Patterns ...
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3
Poisoning Knowledge Graph Embeddings via Relation Inference Patterns ...
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4
Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution Methods ...
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5
Style versus Content: A distinction without a (learnable) difference?
In: International Conference on Computational Linguistics ; https://hal.archives-ouvertes.fr/hal-03112354 ; International Conference on Computational Linguistics, Dec 2020, Virtual, Spain (2020)
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6
Language-Driven Region Pointer Advancement for Controllable Image Captioning ...
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7
Semantic Relatedness and Taxonomic Word Embeddings ...
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8
English WordNet Taxonomic Random Walk Pseudo-Corpora
In: Conference papers (2020)
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9
Language-Driven Region Pointer Advancement for Controllable Image Captioning
In: Conference papers (2020)
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10
Local Alignment of Frame of Reference Assignment in English and Swedish Dialogue
In: Conference papers (2020)
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11
Capturing and measuring thematic relatedness [<Journal>]
DNB Subject Category Language
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12
Synthetic, Yet Natural: Properties of WordNet Random Walk Corpora and the impact of rare words on embedding performance
In: Conference papers (2019)
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13
Size Matters: The Impact of Training Size in Taxonomically-Enriched Word Embeddings
In: Articles (2019)
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14
TEST: A terminology extraction system for technology related terms
In: Conference papers (2019)
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15
Modular Mechanistic Networks: On Bridging Mechanistic and Phenomenological Models with Deep Neural Networks in Natural Language Processing ...
Dobnik, Simon; Kelleher, John D.. - : arXiv, 2018
Abstract: Natural language processing (NLP) can be done using either top-down (theory driven) and bottom-up (data driven) approaches, which we call mechanistic and phenomenological respectively. The approaches are frequently considered to stand in opposition to each other. Examining some recent approaches in deep learning we argue that deep neural networks incorporate both perspectives and, furthermore, that leveraging this aspect of deep learning may help in solving complex problems within language technology, such as modelling language and perception in the domain of spatial cognition. ... : 18 pages, 1 figure, Appears in CLASP Papers in Computational Linguistics Vol. 1: Proceedings of the Conference on Logic and Machine Learning in Natural Language (LaML 2017) ...
Keyword: Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences; Machine Learning cs.LG; Machine Learning stat.ML; Neural and Evolutionary Computing cs.NE
URL: https://dx.doi.org/10.48550/arxiv.1807.09844
https://arxiv.org/abs/1807.09844
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16
What is not where: the challenge of integrating spatial representations into deep learning architectures ...
Kelleher, John D.; Dobnik, Simon. - : arXiv, 2018
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17
Is it worth it? Budget-related evaluation metrics for model selection ...
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18
Is it worth it? Budget-related evaluation metrics for model selection
In: Conference papers (2018)
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19
Exploring the Functional and Geometric Bias of Spatial Relations Using Neural Language Models
In: Conference papers (2018)
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20
Idiom Type Identification with Smoothed Lexical Features and a Maximum Margin Classifier ...
Kelleher, John; Ross, Robert J. And Salton, Giancarlo. - : Dublin Institute of Technology, 2017
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