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Browsing by Subject "Natural Language Processing"
Now showing items 1-7 of 7
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Annotation of phenotypes using ontologies: a gold standard for the training and evaluation of natural language processing systems.
(Database : the journal of biological databases and curation, 2018-01)Natural language descriptions of organismal phenotypes, a principal object of study in biology, are abundant in the biological literature. Expressing these phenotypes as logical statements using ontologies would ... -
Automated Learning of Event Coding Dictionaries for Novel Domains with an Application to Cyberspace
(2016)Event data provide high-resolution and high-volume information about political events. From COPDAB to KEDS, GDELT, ICEWS, and PHOENIX, event datasets and the frameworks that produce them have supported a variety of research ... -
Deep Generative Models for Vision, Languages and Graphs
(2019)Deep generative models have achieved remarkable success in modeling various types of data, ranging from vision, languages and graphs etc. They offer flexible and complementary representations for both labeled and unlabeled ... -
Deep Latent-Variable Models for Natural Language Understanding and Generation
(2020)Deep latent-variable models have been widely adopted to model various types of data, due to its ability to: 1) infer rich high-level information from the input data (especially in a low-resource setting); 2) result in a ... -
Moving the mountain: analysis of the effort required to transform comparative anatomy into computable anatomy.
(Database : the journal of biological databases and curation, 2015-01)The diverse phenotypes of living organisms have been described for centuries, and though they may be digitized, they are not readily available in a computable form. Using over 100 morphological studies, the Phenoscape project ... -
Prediction of Bitcoin prices using Twitter Data and Natural Language Processing
(2021-12-16)The influence of social media platforms like Twitter had long been perceived as a bellwether of Bitcoin Prices. This paper aims to investigate if the tweets can be modeled using two different approaches, namely, the Naïve ... -
Semantic Term “Blurring” and Stochastic “Barcoding” for Improved Unsupervised Text Classification
(2018-04)The abundance of text data being produced in the modern age makes it increasingly important to intuitively group, categorize, or classify text data by theme for efficient retrieval and search. Yet, the high dimensionality ...
