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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 43 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 Language-Driven Region Pointer Advancement for Controllable Image Captioning ... Lindh, Annika; Ross, Robert J.; Kelleher, John D.. - : arXiv, 2020 BASE Show details 3 Semantic Relatedness and Taxonomic Word Embeddings ... Kacmajor, Magdalena; Kelleher, John D.; Klubicka, Filip. - : arXiv, 2020 BASE Show details 4 English WordNet Taxonomic Random Walk Pseudo-Corpora Klubicka, Filip; Maldonado, Alfredo; Mahalunkar, Abhijit... In: Conference papers (2020) BASE Show details 5 Language-Driven Region Pointer Advancement for Controllable Image Captioning Lindh, Annika; Ross, Robert; Kelleher, John D. In: Conference papers (2020) BASE Show details 6 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 7 Capturing and measuring thematic relatedness [<Journal>] Kacmajor, Magdalena Verfasser] DNB Subject Category Language Show details 8 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 9 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 10 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 11 What is not where: the challenge of integrating spatial representations into deep learning architectures ... Kelleher, John D.; Dobnik, Simon. - : arXiv, 2018 BASE Show details 12 Is it worth it? Budget-related evaluation metrics for model selection ... Klubička, Filip; Salton, Giancarlo D.; Kelleher, John D.. - : arXiv, 2018 BASE Show details 13 Is it worth it? Budget-related evaluation metrics for model selection Klubicka, Filip; Salton, Giancarlo; Kelleher, John D. In: Conference papers (2018) Abstract: Projects that set out to create a linguistic resource often do so by using a machine learning model that pre-annotates or filters the content that goes through to a human annotator, before going into the final version of the resource. However, available budgets are often limited, and the amount of data that is available exceeds the amount of annotation that can be done. Thus, in order to optimize the benefit from the invested human work, we argue that the decision on which predictive model one should employ depends not only on generalized evaluation metrics, such as accuracy and F-score, but also on the gain metric. The rationale is that, the model with the highest F-score may not necessarily have the best separation and sequencing of predicted classes, thus leading to the investment of more time and/or money on annotating false positives, yielding zero improvement of the linguistic resource. We exemplify our point with a case study, using real data from a task of building a verb-noun idiom dictionary. We show that in our scenario, given the choice of three systems with varying F-scores, the system with the highest F-score does not yield the highest profits. In other words, we show that the cost-benefit trade off can be more favorable if a system with a lower F-score is employed. Keyword: budget; Computational Engineering; Digital Humanities; F-score; gain; idiom dictionary; idiom identification; linguistic resource creation; model evaluation; Other Computer Engineering URL: https://arrow.tudublin.ie/cgi/viewcontent.cgi?article=1234&context=scschcomcon https://arrow.tudublin.ie/scschcomcon/227 BASE Hide details 14 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 15 Back to the Future: Logic and Machine Learning Kelleher, John D.; Dobnik, Simon In: Conference papers (2017) BASE Show details 16 Robot Perception Errors and Human Resolution Strategies in Situated Human-Robot Dialogue Kelleher, John D.; Mac Namee, Brian; Schütte, Niels In: Articles (2017) BASE Show details 17 Assessing the Usefulness of Different Feature Sets for Predicting the Comprehension Difficulty of Text Mac Namee, Brian; Kelleher, John D.; Fitzpatrick, Noel In: Conference papers (2017) BASE Show details 18 Towards a Computational Model of Frame of Reference Alignment in Swedish Dialogue Dobnik, Simon; Howes, Christine; Kelleher, John D.... In: Conference papers (2016) BASE Show details 19 A Model for Attention-Driven Judgements in Type Theory with Records Dobnik, Simon; Kelleher, John D. In: Conference papers (2016) BASE Show details 20 Fundamentals of Machine Learning for Neural Machine Translation Kelleher, John D. In: Conference papers (2016) BASE Show details Page: 1 2 3

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