105 research outputs found
Verbal Compounding in Korean
Proceedings of the Nineteenth Annual Meeting of the Berkeley Linguistics
Society: General Session and Parasession on Semantic Typology and
Semantic Universals (1993
Improving open spaces surrounding public buildings: focusing on vigibility graph analysis of cases in Sejong
Painsight: An Extendable Opinion Mining Framework for Detecting Pain Points Based on Online Customer Reviews
As the e-commerce market continues to expand and online transactions
proliferate, customer reviews have emerged as a critical element in shaping the
purchasing decisions of prospective buyers. Previous studies have endeavored to
identify key aspects of customer reviews through the development of sentiment
analysis models and topic models. However, extracting specific dissatisfaction
factors remains a challenging task. In this study, we delineate the pain point
detection problem and propose Painsight, an unsupervised framework for
automatically extracting distinct dissatisfaction factors from customer reviews
without relying on ground truth labels. Painsight employs pre-trained language
models to construct sentiment analysis and topic models, leveraging attribution
scores derived from model gradients to extract dissatisfaction factors. Upon
application of the proposed methodology to customer review data spanning five
product categories, we successfully identified and categorized dissatisfaction
factors within each group, as well as isolated factors for each type. Notably,
Painsight outperformed benchmark methods, achieving substantial performance
enhancements and exceptional results in human evaluations.Comment: WASSA at ACL 202
Combined Fluoroscopy- and CT-Guided Transthoracic Needle Biopsy Using a C-Arm Cone-Beam CT System: Comparison with Fluoroscopy-Guided Biopsy
Abstract. We present a novel method for blind separation of instruments in polyphonic music based on a non-negative matrix factor 2-D deconvolution algorithm. Using a model which is convolutive in both time and frequency we factorize a spectrogram representation of music into components corresponding to individual instruments. Based on this factorization we separate the instruments using spectrogram masking. The proposed algorithm has applications in computational auditory scene analysis, music information retrieval, and automatic music transcription.
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