ARC
发表于 2025-3-23 12:38:49
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完成才能战胜
发表于 2025-3-23 15:31:33
Introduction,communication. Since manual extraction of such information can be prohibitively expensive, it has become obvious to automatize the process of information gathering from large-scale text. And, NLP provides ways to do that.
glacial
发表于 2025-3-23 19:14:45
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nitric-oxide
发表于 2025-3-23 23:51:40
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Tempor
发表于 2025-3-24 04:23:09
Book 2018authors employ eye-tracking technology to record and analyze shallow cognitive information in the form of gaze patterns of readers/annotators who perform language processing tasks. The insights gained from such measures are subsequently translated into systems that help us (1) assess the actual cogn
多节
发表于 2025-3-24 10:02:13
Harnessing Cognitive Features for Sentiment Analysis and Sarcasm Detections used for well-known supervised machine learning-based NLP systems, improve the performance of such systems. We stick to the tasks of sentiment analysis and sarcasm detection—two well-known problems in text classification.
征税
发表于 2025-3-24 12:13:18
https://doi.org/10.1007/978-3-662-53620-9gh total fixation/saccade duration may seem robust under the assumption that “complex tasks require more time,” it seems more intuitive to consider the complexity of eye-movement patterns in their entirety to derive such labels.
osteocytes
发表于 2025-3-24 16:29:52
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声明
发表于 2025-3-24 23:04:45
Scanpath Complexity: Modeling Reading/Annotation Effort Using Gaze Informationgh total fixation/saccade duration may seem robust under the assumption that “complex tasks require more time,” it seems more intuitive to consider the complexity of eye-movement patterns in their entirety to derive such labels.
严厉谴责
发表于 2025-3-25 02:30:12
Predicting Readers’ Sarcasm Understandability by Modeling Gaze Behaviorappeared to be subtle when the text had linguistic nuances like sarcasm, which the annotators failed to recognize. This motivated us to work on a highly specific yet important problem of sarcasm understandability prediction—a starting step toward an even more important problem of modeling text comprehensibility.