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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Page: 1 2 3 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) BASE Show details 2 Poisoning Knowledge Graph Embeddings via Relation Inference Patterns ... Bhardwaj, Peru; Kelleher, John; Costabello, Luca. - : arXiv, 2021 BASE Show details 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 BASE Show details 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 BASE Show details 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) BASE Show details 6 Language-Driven Region Pointer Advancement for Controllable Image Captioning ... Lindh, Annika; Ross, Robert J.; Kelleher, John D.. - : arXiv, 2020 BASE Show details 7 Semantic Relatedness and Taxonomic Word Embeddings ... Kacmajor, Magdalena; Kelleher, John D.; Klubicka, Filip. - : arXiv, 2020 BASE Show details 8 English WordNet Taxonomic Random Walk Pseudo-Corpora Klubicka, Filip; Maldonado, Alfredo; Mahalunkar, Abhijit... In: Conference papers (2020) BASE Show details 9 Language-Driven Region Pointer Advancement for Controllable Image Captioning Lindh, Annika; Ross, Robert; Kelleher, John D. In: Conference papers (2020) BASE Show details 10 Local Alignment of Frame of Reference Assignment in English and Swedish Dialogue Dobnik, Simon; Kelleher, John D.; Howes, Christine In: Conference papers (2020) BASE Show details 11 Capturing and measuring thematic relatedness [<Journal>] Kacmajor, Magdalena Verfasser] DNB Subject Category Language Show details 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) BASE Show details 13 Size Matters: The Impact of Training Size in Taxonomically-Enriched Word Embeddings Maldonado, Alfredo; Klubicka, Filip; Kelleher, John D. In: Articles (2019) BASE Show details 14 TEST: A terminology extraction system for technology related terms Hossari, Murhaf; Dev, Soumyabrata; Kelleher, John In: Conference papers (2019) Abstract: Tracking developments in the highly dynamic data-technology landscape are vital to keeping up with novel technologies and tools, in the various areas of Artificial Intelligence (AI). However, It is difficult to keep track of all the relevant technology keywords. In this paper, we propose a novel system that addresses this problem. This tool is used to automatically detect the existence of new technologies and tools in text, and extract terms used to describe these new technologies. The extracted new terms can be logged as new AI technologies as they are found on-the-fly in the web. It can be subsequently classified into the relevant semantic labels and AI domains. Our proposed tool is based on a two-stage cascading model--the first stage classifies if the sentence contains a technology term or not; and the second stage identifies the technology keyword in the sentence. We obtain a competitive accuracy for both tasks of sentence classification and text identification. Keyword: Computer Sciences; natural language processing; term extraction; TEST; Text classification URL: https://arrow.tudublin.ie/cgi/viewcontent.cgi?article=1001&context=adaptcon https://arrow.tudublin.ie/adaptcon/2 BASE Hide details 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 BASE Show details 16 What is not where: the challenge of integrating spatial representations into deep learning architectures ... Kelleher, John D.; Dobnik, Simon. - : arXiv, 2018 BASE Show details 17 Is it worth it? Budget-related evaluation metrics for model selection ... Klubička, Filip; Salton, Giancarlo D.; Kelleher, John D.. - : arXiv, 2018 BASE Show details 18 Is it worth it? Budget-related evaluation metrics for model selection Klubicka, Filip; Salton, Giancarlo; Kelleher, John D. In: Conference papers (2018) BASE Show details 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) BASE Show details 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 BASE Show details Page: 1 2 3

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