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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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Abstract:
Creating a linguistic resource is often done by using a machine learning model that filters the content that goes through to a human annotator, before going into the final resource. However, budgets are often limited, and the amount of available data exceeds the amount of affordable annotation. In order to optimize the benefit from the invested human work, we argue that deciding on which model one should employ depends not only on generalized evaluation metrics such as F-score, but also on the gain metric. Because the model with the highest F-score may not necessarily have the best sequencing of predicted classes, this may lead to wasting funds 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, 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, in our case ... : 7 pages, 1 figure, 5 tables, In proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018) ...
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Keyword:
Computation and Language cs.CL; FOS Computer and information sciences
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URL: https://arxiv.org/abs/1807.06998 https://dx.doi.org/10.48550/arxiv.1807.06998
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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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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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