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Characterizing News Portrayal of Civil Unrest in Hong Kong, 1998–2020 ...
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ArgFuse: A Weakly-Supervised Framework for Document-Level Event Argument Aggregation ...
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Reordering Examples Helps during Priming-based Few-Shot Learning ...
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One Teacher is Enough? Pre-trained Language Model Distillation from Multiple Teachers ...
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On the Distribution, Sparsity, and Inference-time Quantization of Attention Values in Transformers ...
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Knowledge-based neural pre-training for Intelligent Document Management ...
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Improving Machine Translation of Arabic Dialects through Multi-Task Learning ...
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Team “NoConflict” at CASE 2021 Task 1: Pretraining for Sentence-Level Protest Event Detection ...
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DAAI at CASE 2021 Task 1: Transformer-based Multilingual Socio-political and Crisis Event Detection ...
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Hell Hath No Fury? Correcting Bias in the NRC Emotion Lexicon ...
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Abstract:
There have been several attempts to create an accurate and thorough emotion lexicon in English, which identifies the emotional content of words. Of the several commonly used resources, the NRC emotion lexicon has received the most attention due to its availability, size, and its choice of Plutchik's expressive 8-class emotion model. In this paper we identify a large number of troubling entries in the NRC lexicon, where words that should in most contexts be emotionally neutral, with no affect (e.g., lesbian, stone, mountain), are associated with emotional labels that are inaccurate, nonsensical, pejorative, or, at best, highly contingent and context-dependent (e.g., lesbian labeled as disgust and sadness, stone as anger, or mountain as anticipation). We describe a procedure for semi-automatically correcting these problems in the NRC, which includes disambiguating POS categories and aligning NRC entries with other emotion lexicons to infer the accuracy of labels. We demonstrate via an experimental benchmark ...
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Keyword:
Computational Linguistics; Condensed Matter Physics; FOS Physical sciences; Information and Knowledge Engineering; Machine Learning; Neural Network; Semantics
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URL: https://underline.io/lecture/29885-hell-hath-no-furyquestion-correcting-bias-in-the-nrc-emotion-lexicon https://dx.doi.org/10.48448/b0ny-0b91
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System Description for the CommonGen task with the POINTER model ...
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Compositional Lexical Semantics In Natural Language Inference
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In: Publicly Accessible Penn Dissertations (2017)
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A Uniform Approach to Analogies, Synonyms, Antonyms, and Associations
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The Missing Link between Morphemic Assemblies and Behavioral Responses:a Bayesian Information-Theoretical model of lexical processing
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