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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)
Abstract: Creating word embeddings that reflect semantic relationships encoded in lexical knowledge resources is an open challenge. One approach is to use a random walk over a knowledge graph to generate a pseudo-corpus and use this corpus to train embeddings. However, the effect of the shape of the knowledge graph on the generated pseudo-corpora, and on the resulting word embeddings, has not been studied. To explore this, we use English WordNet, constrained to the taxonomic (tree-like) portion of the graph, as a case study. We investigate the properties of the generated pseudo-corpora, and their impact on the resulting embeddings. We find that the distributions in the psuedo-corpora exhibit properties found in natural corpora, such as Zipf’s and Heaps’ law, and also ob- serve that the proportion of rare words in a pseudo-corpus affects the performance of its embeddings on word similarity.
Keyword: Artificial Intelligence and Robotics; Computational Linguistics; corpus; evaluation; Numerical Analysis and Scientific Computing; random walk; representations; Software Engineering; taxonomy; word embeddings; word similarity; WordNet
URL: https://arrow.tudublin.ie/scschcomcon/271
https://arrow.tudublin.ie/cgi/viewcontent.cgi?article=1283&context=scschcomcon
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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
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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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