Results 21 to 30 of about 612 (260)
, 2016 Textual entailment is a binary relation between two natural-language texts (called ‘text’ and ‘hypothesis’), where readers of the ‘text’ would agree the ‘hypothesis’ is most likely true (Peter is snoring → A man sleeps). Its recognition requires an account of linguistic variability ( an event may be realized in different ways, e.g. Peter buys the car ↔
Sebastian Padó, Ido Dagan
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A Cross-Lingual Hybrid Neural Network With Interaction Enhancement for Grading Short-Answer Texts
IEEE Access, 2023 Automatic Short-Answer Grading (ASAG) is an application for recognizing textual entailment in smart education. With the continuous expansion of the application scope of artificial neural networks, many deep learning models have been applied to grading ...
Yishan Chen +4 more
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CoRR, 2018 Capturing semantic relations between sentences, such as entailment, is a long-standing challenge for computational semantics. Logic-based models analyse entailment in terms of possible worlds (interpretations, or situations) where a premise P entails a hypothesis H iff in all worlds where P is true, H is also true.
Hoa Trong Vu +8 more
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Causality Mining in Natural Languages Using Machine and Deep Learning Techniques: A Survey
Applied Sciences, 2021 The era of big textual corpora and machine learning technologies have paved the way for researchers in numerous data mining fields. Among them, causality mining (CM) from textual data has become a significant area of concern and has more attention from ...
Wajid Ali +4 more
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A lightweight text entailment model
四川大学学报. 自然科学版, 2021 Most of the existing mainstream textual entailment models adopt recurrent neutral network to encode text, and various complex attention mechanisms or manually extracted text features are used to improve the accuracy of textual entailment recognition. The
WANG Wei +5 more
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Novelty Detection: A Perspective from Natural Language Processing
Computational Linguistics, 2022 The quest for new information is an inborn human trait and has always been quintessential for human survival and progress. Novelty drives curiosity, which in turn drives innovation.
Tirthankar Ghosal +4 more
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Engineering ProceedingsThis paper investigates the domain of Natural Language Inference (NLI), with an emphasis on Recognizing Textual Entailment (RTE). We utilize the Stanford Natural Language Inference (SNLI) dataset, a benchmark for RTE tasks, to examine the efficacy of ...
Franklin Quilumba +2 more
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BioASQ-QA: A manually curated corpus for Biomedical Question Answering
Scientific Data, 2023 The BioASQ question answering (QA) benchmark dataset contains questions in English, along with golden standard (reference) answers and related material.
Anastasia Krithara +3 more
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Transactions of the Association for Computational Linguistics, 2021 Despite the progress made in recent years in addressing natural language understanding (NLU) challenges, the majority of this progress remains to be concentrated on resource-rich languages like English.
Daniel Khashabi +24 more
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Pediatric Blood &Cancer, EarlyView.ABSTRACT Background Medication nonadherence during the first 100 days after pediatric hematopoietic stem cell transplantation (HSCT) and during oncology treatment increases risk for complications. BMT4me is a caregiver‐facing mobile health (mHealth) application providing medication reminders, symptom tracking, and note‐taking features to support ...
Micah A. Skeens +4 more
wiley +1 more source