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Compression, Transduction, and Creation: A Unified Framework for Evaluating Natural Language Generation ...
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Discourse in Multimedia: A Case Study in Extracting Geometry Knowledge from Textbooks
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In: Computational Linguistics, Vol 45, Iss 4, Pp 627-665 (2020) (2020)
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Discovering Sociolinguistic Associations with Structured Sparsity ...
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Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation ...
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Discourse in Multimedia: A Case Study in Information Extraction ...
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Adversarial Connective-exploiting Networks for Implicit Discourse Relation Classification ...
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Recurrent Topic-Transition GAN for Visual Paragraph Generation ...
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Symmetric Correspondence Topic Models for Multilingual Text Analysis ...
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Symmetric Correspondence Topic Models for Multilingual Text Analysis ...
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Discovering Sociolinguistic Associations with Structured Sparsity ...
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Abstract:
We present a method to discover robust and interpretable sociolinguistic associations from raw geotagged text data. Using aggregate demographic statistics about the authors' geographic communities, we solve a multi-output regression problem between demographics and lexical frequencies. By imposing a composite ℓ 1,∞ regularizer, we obtain structured sparsity, driving entire rows of coefficients to zero. We perform two regression studies. First, we use term frequencies to predict demographic attributes; our method identifies a compact set of words that are strongly associated with author demographics. Next, we conjoin demographic attributes into features , which we use to predict term frequencies. The composite regularizer identifies a small number of features, which correspond to communities of authors united by shared demographic and linguistic properties ...
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Keyword:
170203 Knowledge Representation and Machine Learning; FOS Psychology
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URL: https://dx.doi.org/10.1184/r1/6475556 https://kilthub.cmu.edu/articles/Discovering_Sociolinguistic_Associations_with_Structured_Sparsity/6475556
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A Latent Variable Model for Geographic Lexical Variation ...
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A Latent Variable Model for Geographic Lexical Variation ...
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Learning Structured Classifiers with Dual Coordinate Ascent
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In: DTIC (2010)
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