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Shapley Idioms: Analysing BERT Sentence Embeddings for General Idiom Token Identification
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In: Front Artif Intell (2022)
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Poisoning Knowledge Graph Embeddings via Relation Inference Patterns ...
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Poisoning Knowledge Graph Embeddings via Relation Inference Patterns ...
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Adversarial Attacks on Knowledge Graph Embeddings via Instance Attribution Methods ...
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Style versus Content: A distinction without a (learnable) difference?
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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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Language-Driven Region Pointer Advancement for Controllable Image Captioning ...
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English WordNet Taxonomic Random Walk Pseudo-Corpora
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In: Conference papers (2020)
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Language-Driven Region Pointer Advancement for Controllable Image Captioning
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In: Conference papers (2020)
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Local Alignment of Frame of Reference Assignment in English and Swedish Dialogue
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In: Conference papers (2020)
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Synthetic, Yet Natural: Properties of WordNet Random Walk Corpora and the impact of rare words on embedding performance
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In: Conference papers (2019)
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Size Matters: The Impact of Training Size in Taxonomically-Enriched Word Embeddings
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In: Articles (2019)
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TEST: A terminology extraction system for technology related terms
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In: Conference papers (2019)
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Modular Mechanistic Networks: On Bridging Mechanistic and Phenomenological Models with Deep Neural Networks in Natural Language Processing ...
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What is not where: the challenge of integrating spatial representations into deep learning architectures ...
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Is it worth it? Budget-related evaluation metrics for model selection ...
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Is it worth it? Budget-related evaluation metrics for model selection
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In: Conference papers (2018)
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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.
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Keyword:
budget; Computational Engineering; Digital Humanities; F-score; gain; idiom dictionary; idiom identification; linguistic resource creation; model evaluation; Other Computer Engineering
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URL: https://arrow.tudublin.ie/cgi/viewcontent.cgi?article=1234&context=scschcomcon https://arrow.tudublin.ie/scschcomcon/227
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Exploring the Functional and Geometric Bias of Spatial Relations Using Neural Language Models
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In: Conference papers (2018)
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Idiom Type Identification with Smoothed Lexical Features and a Maximum Margin Classifier ...
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