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Machine learning for semi-automated scoping reviews
Intelligent Systems with Applications, 2023 Scoping reviews are a type of research synthesis that aim to map the literature on a particular topic or research area. Though originally intended to provide a quick overview of a field of research, scoping review teams have been overwhelmed in recent ...
Sharon Mozgai +6 more
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Unsupervised Text Topic-Related Gene Extraction for Large Unbalanced Datasets
, 2020 There is a common notion that traditional unsupervised feature extraction algorithms follow the assumption that the distribution of the different clusters in a dataset is balanced.
Jing-Tao, Sun +11 more
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Guided Semi-Supervised Non-Negative Matrix Factorization
Algorithms, 2022 Classification and topic modeling are popular techniques in machine learning that extract information from large-scale datasets. By incorporating a priori information such as labels or important features, methods have been developed to perform ...
Pengyu Li +6 more
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MMT: A Multilingual and Multi-Topic Indian Social Media Dataset
Proceedings of the First Workshop on Cross-Cultural Considerations in NLP (C3NLP), 2023 Social media plays a significant role in cross-cultural communication. A vast amount of this occurs in code-mixed and multilingual form, posing a significant challenge to Natural Language Processing (NLP) tools for processing such information, like language identification, topic modeling, and named-entity recognition.
Dwip Dalal +2 more
openaire +2 more sources
Predicting protein function via multi-label supervised topic model on gene ontology
Biotechnology & Biotechnological Equipment, 2017 As the biological datasets accumulate rapidly, computational methods designed to automate protein function prediction are critically needed. The problem of protein function prediction can be considered as a multi-label classification problem resulting in
Lin Liu +4 more
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TLATR: Automatic Topic Labeling Using Automatic (Domain-Specific) Term Recognition
IEEE Access, 2021 Topic modeling is a probabilistic graphical model for discovering latent topics in text corpora by using multinomial distributions of topics over words. Topic labeling is used to assign meaningful labels for the discovered topics.
Ciprian-Octavian Truica +1 more
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Normalized Datasets of Harnack’s Reconstruction of Marcion’s 'Gospel'
Journal of Open Humanities Data, 2021 These two datasets are the first born-digital, normalized, peer-reviewed datasets of Harnack’s classic reconstruction of Marcion’s 'Gospel'. The first consists of human-readable postclassical Greek, the second of lemmatized and morphologically tagged ...
Mark G. Bilby
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IEEE AccessTraditional text classification models, such as text kernels, primarily consider the syntactic aspects of text data. This paper introduces Topic-Weighted Kernels, a new text analytics framework that combines global topical themes with word-level ...
Nikhil V. Chandran +2 more
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Topic-Conversation Relevance (TCR) Dataset and Benchmarks
Advances in Neural Information Processing Systems 37To be published in 38th Conference on Neural Information Processing Systems (NeurIPS 2024) Track on Datasets and ...
Yaran Fan +3 more
openaire +3 more sources
Concept Extraction and Clustering for Topic Digital Library Construction [PDF]
, 2008 This paper is to introduce a new approach to build topic digital library using concept extraction and document clustering. Firstly, documents in a special domain are automatically produced by document classification approach.
Dan, Wu, Chengzhi, Zhang
core