Results 1 to 10 of about 52,865,053 (238)
A Novel Hierarchical Topic Model for Horizontal Topic Expansion With Observed Label Information
IEEE Access, 2019 Hierarchical topic models, such as hierarchical Latent Dirichlet Allocation (hLDA)and its variations, can organize topics into a hierarchy automatically.
Xi Zou +4 more
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A latent topic‐aware network for dense video captioning
IET Computer Vision, 2023 Multiple events in a long untrimmed video possess the characteristics of similarity and continuity. These characteristics can be considered as a kind of topic semantic information, which probably behaves as same sports, similar scenes, same objects etc ...
Tao Xu +3 more
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Exploiting Long-Term Dependency for Topic Sentiment Analysis
IEEE Access, 2020 Most existing unsupervised approaches to detect topic sentiment in social texts consider only the text sequences in corpus and put aside social dynamics, as leads to algorithm’s disability to discover true sentiment of social users. To address the
Faliang Huang +3 more
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Mining academic publications to automatically identify data sources
International Journal of Population Data Science, 2018 Background Discovering suitable datasets is an important part of health research, particularly for projects working with cohort data, but with the proliferation of so many national and international initiatives, it is becoming increasingly difficult for ...
Athanasios Anastasiou, Karen Tingay
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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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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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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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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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Extracting a Topic Specific Dataset from a Twitter Archive [PDF]
, 2015 Datasets extracted from the microblogging service Twitter are often generated using specific query terms or hashtags. We describe how a dataset produced using the query term ‘syria’ can be increased in size to include tweets on the topic of Syria that do not contain that query term.
Clare Llewellyn +4 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
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