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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 Hits 1 – 7 of 7 1 English WordNet Taxonomic Random Walk Pseudo-Corpora Klubicka, Filip; Maldonado, Alfredo; Mahalunkar, Abhijit... In: Conference papers (2020) BASE Show details 2 Language related issues for machine translation between closely related south Slavic languages Arcan, Mihael; Klubicka, Filip; Popovic, Maja. - : The COLING 2016 Organizing Committee, 2019 BASE Show details 3 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 4 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 5 Quantitative Fine-grained Human Evaluation of Machine Translation Systems: a Case Study on English to Croatian Sanchez-Cartagena, Victor Manuel; Toral, Antonio; Klubicka, Filip In: Articles (2018) BASE Show details 6 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 7 hr500k – A Reference Training Corpus of Croatian. Erjavec, Tomaž; Ljubešić, Nikola; Klubicka, Filip... In: Conference papers (2018) BASE Show details

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