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Hits 1 – 20 of 51

1	Shapley Idioms: Analysing BERT Sentence Embeddings for General Idiom Token Identification
	Nedumpozhimana, Vasudevan; Klubička, Filip; Kelleher, John D.
	In: Front Artif Intell (2022)
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2	Poisoning Knowledge Graph Embeddings via Relation Inference Patterns ...
	Bhardwaj, Peru; Kelleher, John; Costabello, Luca. - : arXiv, 2021
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3	Poisoning Knowledge Graph Embeddings via Relation Inference Patterns ...
	The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Bhardwaj, Peru; Costabello, Luca. - : Underline Science Inc., 2021
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4	Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution Methods ...
	The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Bhardwaj, Peru; Costabello, Luca. - : Underline Science Inc., 2021
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5	Style versus Content: A distinction without a (learnable) difference?
	Jafaritazehjani, Somayeh; Lecorvé, Gwénolé; Lolive, Damien...
	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 ...
	Lindh, Annika; Ross, Robert J.; Kelleher, John D.. - : arXiv, 2020
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7	Semantic Relatedness and Taxonomic Word Embeddings ...
	Kacmajor, Magdalena; Kelleher, John D.; Klubicka, Filip. - : arXiv, 2020
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8	English WordNet Taxonomic Random Walk Pseudo-Corpora
	Klubicka, Filip; Maldonado, Alfredo; Mahalunkar, Abhijit...
	In: Conference papers (2020)
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9	Language-Driven Region Pointer Advancement for Controllable Image Captioning
	Lindh, Annika; Ross, Robert; Kelleher, John D.
	In: Conference papers (2020)
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10	Local Alignment of Frame of Reference Assignment in English and Swedish Dialogue
	Dobnik, Simon; Kelleher, John D.; Howes, Christine
	In: Conference papers (2020)
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11	Capturing and measuring thematic relatedness [<Journal>]
	Kacmajor, Magdalena Verfasser]
	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
	Klubicka, Filip; Mahalunkar, Abhijit; Maldonado, Alfredo...
	In: Conference papers (2019)
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13	Size Matters: The Impact of Training Size in Taxonomically-Enriched Word Embeddings
	Maldonado, Alfredo; Klubicka, Filip; Kelleher, John D.
	In: Articles (2019)
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14	TEST: A terminology extraction system for technology related terms
	Hossari, Murhaf; Dev, Soumyabrata; Kelleher, John
	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 ...
	Klubička, Filip; Salton, Giancarlo D.; Kelleher, John D.. - : arXiv, 2018
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18	Is it worth it? Budget-related evaluation metrics for model selection
	Klubicka, Filip; Salton, Giancarlo; Kelleher, John D.
	In: Conference papers (2018)
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19	Exploring the Functional and Geometric Bias of Spatial Relations Using Neural Language Models
	Dobnik, Simon; Ghanimifard, Mehdi; Kelleher, John D.
	In: Conference papers (2018)
	Abstract: The challenge for computational models of spatial descriptions for situated dialogue systems is the integration of information from different modalities. The semantics of spatial descriptions are grounded in at least two sources of information: (i) a geometric representation of space and (ii) the functional interaction of related objects that. We train several neural language models on descriptions of scenes from a dataset of image captions and examine whether the functional or geometric bias of spatial descriptions reported in the literature is reflected in the estimated perplexity of these models. The results of these experiments have implications for the creation of models of spatial lexical semantics for human-robot dialogue systems. Furthermore, they also provide an insight into the kinds of the semantic knowledge captured by neural language models trained on spatial descriptions, which has implications for image captioning systems.
	Keyword: Artificial Intelligence and Robotics; Computational Linguistics; Computer Sciences; Linguistics
	URL: https://arrow.tudublin.ie/airccon/10 https://arrow.tudublin.ie/cgi/viewcontent.cgi?article=1011&context=airccon
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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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