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The augmented social scientist: Using sequential transfer learning to annotate millions of texts with human-level accuracy
The last decade witnessed a spectacular rise in the volume of available textual data. With
this new abundance came the question of how to analyze it. In the social sciences, scholars …
this new abundance came the question of how to analyze it. In the social sciences, scholars …
QuaPy: A Python-based framework for quantification
QuaPy is an open-source framework for performing quantification (aka supervised
prevalence estimation), written in Python. Quantification is the task of training quantifiers via …
prevalence estimation), written in Python. Quantification is the task of training quantifiers via …
[كتاب][B] Learning to quantify
A Esuli, A Fabris, A Moreo, F Sebastiani - 2023 - library.oapen.org
This open access book provides an introduction and an overview of learning to quantify (aka
“quantification”), ie the task of training estimators of class proportions in unlabeled data by …
“quantification”), ie the task of training estimators of class proportions in unlabeled data by …
Tweet sentiment quantification: An experimental re-evaluation
Sentiment quantification is the task of training, by means of supervised learning, estimators
of the relative frequency (also called “prevalence”) of sentiment-related classes (such as …
of the relative frequency (also called “prevalence”) of sentiment-related classes (such as …
A survey of recent machine learning techniques for stock prediction methodologies
The prime purpose of the research is to investigate stock price prediction techniques and
their shortcomings concerning particular characteristics and performance measures. The …
their shortcomings concerning particular characteristics and performance measures. The …
MC-SQ: A highly accurate ensemble for multi-class quantification
Quantification research proposes methods to estimate the class distribution in an
independent sample. Many areas, such as epidemiology, sentiment analysis, political …
independent sample. Many areas, such as epidemiology, sentiment analysis, political …
[PDF][PDF] Pitfalls in quantification assessment
Quantification is a research area that develops methods that estimate the class attribute
prevalence in an independent sample. Like the other fields in Machine Learning …
prevalence in an independent sample. Like the other fields in Machine Learning …
Quantification using permutation-invariant networks based on histograms
Quantification, also known as class prevalence estimation, is the supervised learning task in
which a model is trained to predict the prevalence of each class in a given bag of examples …
which a model is trained to predict the prevalence of each class in a given bag of examples …
Multi-label quantification
Quantification, variously called supervised prevalence estimation or learning to quantify, is
the supervised learning task of generating predictors of the relative frequencies (aka …
the supervised learning task of generating predictors of the relative frequencies (aka …
Accurate and Well-Calibrated ICD Code Assignment Through Attention Over Diverse Label Embeddings
Although the International Classification of Diseases (ICD) has been adopted worldwide,
manually assigning ICD codes to clinical text is time-consuming, error-prone, and …
manually assigning ICD codes to clinical text is time-consuming, error-prone, and …