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Tecnologica cosa: Modeling Storyteller Personalities in Boccaccio's Decameron ...
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Bad Seeds: Evaluating Lexical Methods for Bias Measurement ...
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Domain-Specific Lexical Grounding in Noisy Visual-Textual Documents ...
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Abstract:
Images can give us insights into the contextual meanings of words, but current image-text grounding approaches require detailed annotations. Such granular annotation is rare, expensive, and unavailable in most domain-specific contexts. In contrast, unlabeled multi-image, multi-sentence documents are abundant. Can lexical grounding be learned from such documents, even though they have significant lexical and visual overlap? Working with a case study dataset of real estate listings, we demonstrate the challenge of distinguishing highly correlated grounded terms, such as "kitchen" and "bedroom", and introduce metrics to assess this document similarity. We present a simple unsupervised clustering-based method that increases precision and recall beyond object detection and image tagging baselines when evaluated on labeled subsets of the dataset. The proposed method is particularly effective for local contextual meanings of a word, for example associating "granite" with countertops in the real estate dataset and ...
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Keyword:
Computation and Language cs.CL; Computer Vision and Pattern Recognition cs.CV; FOS Computer and information sciences
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URL: https://arxiv.org/abs/2010.16363
https://dx.doi.org/10.48550/arxiv.2010.16363
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Understanding Word Embedding Stability Across Languages and Applications
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How We Do Things With Words: Analyzing Text as Social and Cultural Data
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In: Front Artif Intell (2020)
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