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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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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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Abstract:
Recognizing analogies, synonyms, antonyms, and associations appear to be four distinct tasks, requiring distinct NLP algorithms. In the past, the four tasks have been treated independently, using a wide variety of algorithms. These four semantic classes, however, are a tiny sample of the full range of semantic phenomena, and we cannot afford to create ad hoc algorithms for each semantic phenomenon; we need to seek a unified approach. We propose to subsume a broad range of phenomena under analogies. To limit the scope of this paper, we restrict our attention to the subsumption of synonyms, antonyms, and associations. We introduce a supervised corpus-based machine learning algorithm for classifying analogous word pairs, and we show that it can solve multiple-choice SAT analogy questions, TOEFL synonym questions, ESL synonym-antonym questions, and similar-associated-both questions from cognitive psychology.
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Keyword:
Artificial Intelligence; Computational Linguistics; Language; Machine Learning; Semantics
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URL: http://cogprints.org/6181/
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The Latent Relation Mapping Engine: Algorithm and Experiments [<Journal>]
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