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Profiling and functional characterization of maternal mRNA translation during mouse maternal-to-zygotic transition
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In: Sci Adv (2022)
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Lexical activation in late bilinguals: effects of phonological neighbourhood on spoken word production
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Migrants’ narratives on urban governance: a case from Kolkata, a city of the global south
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The history of number words in the world's languages-what have we learnt so far?
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Sensory Descriptor Analysis of Whisky Lexicons through the Use of Deep Learning ; FOODS
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Abstract:
This paper is concerned with extracting relevant terms from a text corpus on whisk(e)y. Relevant terms are usually contextually defined in their domain of use. Arguably, every domain has a specialized vocabulary used for describing things. For example, the field of Sensory Science, a sub-field of Food Science, investigates human responses to food products and differentiates descriptive terms for flavors from ordinary, non-descriptive language. Within the field, descriptors are generated through Descriptive Analysis, a method wherein a human panel of experts tastes multiple food products and defines descriptors. This process is both time-consuming and expensive. However, one could leverage existing data to identify and build a flavor language automatically. For example, there are thousands of professional and semi-professional reviews of whisk(e)y published on the internet, providing abundant descriptors interspersed with non-descriptive language. The aim, then, is to be able to automatically identify descriptive terms in unstructured reviews for later use in product flavor characterization. We created two systems to perform this task. The first is an interactive visual tool that can be used to tag examples of descriptive terms from thousands of whisky reviews. This creates a training dataset that we use to perform transfer learning using GloVe word embeddings and a Long Short-Term Memory deep learning model architecture. The result is a model that can accurately identify descriptors within a corpus of whisky review texts with a train/test accuracy of 99% and precision, recall, and F1-scores of 0.99. We tested for overfitting by comparing the training and validation loss for divergence. Our results show that the language structure for descriptive terms can be programmatically learned. ; Published (Publication status)
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Keyword:
0908 Food Sciences; deep learning; flavor lexicon; Food Science & Technology; Life Sciences & Biomedicine; long short-term memory; natural language processing; Science & Technology; sensory science; TASTE
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URL: https://doi.org/10.3390/foods10071633 http://hdl.handle.net/10919/104680
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A novel hybrid multi-criteria group decision making approach for failure mode and effect analysis: An essential requirement for sustainable manufacturing
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“Giving yourself some breathing room…”: an exploration of group meditation for people with aphasia
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Modeling of the near to far acoustic fields of an echolocating bottlenose dolphin and harbor porpoisea)
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A recipe for success? A nutrient analysis of recipes promoted by supermarkets
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Waiting for speech-language pathology services: a randomised controlled trial comparing therapy, advice and device
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Health-related quality of life, service utilization and costs of low language: A systematic review
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Distress and problem assessment among people living with cancer from Culturally and Linguistically Diverse backgrounds
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Adapting culturally appropriate mental health screening tools for use among conflict-affected and other vulnerable adolescents in Nigeria
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First-order approximation to English, second-order approximation to English, and orthographic neighbor ratio norms for 925 nouns
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Rubin, DC. - : Springer Science and Business Media LLC, 2019
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Relevance of mindfulness practices for culturally and linguistically diverse cancer populations
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Weight status and obesity-related dietary behaviours among culturally and linguistically diverse (CALD) children in Victoria, Australia
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Beyond the clinic? Eluding a medical diagnosis of anorexia through narrative
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Anomalous morphology in left hemisphere motor and premotor cortex of children who stutter
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