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Do Syntactic Probes Probe Syntax? Experiments with Jabberwocky Probing
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In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2021)
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What About the Precedent: An Information-Theoretic Analysis of Common Law
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In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2021)
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Finding Concept-specific Biases in Form–Meaning Associations
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In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2021)
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A Non-Linear Structural Probe
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In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2021)
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How (Non-)Optimal is the Lexicon?
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In: Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (2021)
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Consumer Cynicism Identification for Spanish Reviews using a Spanish Transformer Model ; Identificación del cinismo del consumidor para reseñas en español utilizando un modelo de transformador español
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Backtranslation feedback improves user confidence in MT, not quality
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From Characters to Time Intervals: New Paradigms for Evaluation and Neural Parsing of Time Normalizations
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In: Trans Assoc Comput Linguist (2018)
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UArizona at the CLEF eRisk 2017 Pilot Task: Linear and Recurrent Models for Early Depression Detection
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TAXI at SemEval-2016 Task 13: a taxonomy induction method based on lexico-syntactic patterns, substrings and focused crawling
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Temporal Annotation in the Clinical Domain.
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Styler, William F; Bethard, Steven; Finan, Sean; Palmer, Martha; Pradhan, Sameer; de Groen, Piet C; Erickson, Brad; Miller, Timothy; Lin, Chen; Savova, Guergana; Pustejovsky, James
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In: Transactions of the Association for Computational Linguistics, vol 2 (2014)
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Abstract:
This article discusses the requirements of a formal specification for the annotation of temporal information in clinical narratives. We discuss the implementation and extension of ISO-TimeML for annotating a corpus of clinical notes, known as the THYME corpus. To reflect the information task and the heavily inference-based reasoning demands in the domain, a new annotation guideline has been developed, "the THYME Guidelines to ISO-TimeML (THYME-TimeML)". To clarify what relations merit annotation, we distinguish between linguistically-derived and inferentially-derived temporal orderings in the text. We also apply a top performing TempEval 2013 system against this new resource to measure the difficulty of adapting systems to the clinical domain. The corpus is available to the community and has been proposed for use in a SemEval 2015 task.
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Keyword:
Artificial Intelligence and Image Processing; Cognitive Sciences; Linguistics
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URL: https://escholarship.org/uc/item/9sk3m36q
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Discovering body site and severity modifiers in clinical texts
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An Annotation Framework for Dense Event Ordering
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In: DTIC (2014)
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