Topic-bridged PLSA for cross-domain text classification

By Gui-Rong Xue, Wenyuan Dai, Qiang Yang, Yong Yu. 2008

219 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Marginalized Denoising Autoencoders for Domain Adaptation (2012, methodology): “Domain adaptation (Ben-David et al., 2009; Huang et al., 2007; Weinberger et al., 2009; Xue et al., 2008) aims to generalize a classiﬁer that is trained on a source domain, for which typically plenty of training data is available, to a target domain, for which data is scarce.”
A Comprehensive Survey on Transfer Learning (2019, methodology): “proposes a cross-domain text classification approach termed Topic-Bridged Probabilistic Latent Semantic Analysis (TPLSA) [82].”
Mining topics in documents: standing on the shoulders of big data (2014, methodology): “Topic models have been used to help transfer learning [27, 34].”
Bilingual Co-Training for Sentiment Classification of Chinese Product Reviews (2011, methodology): “To date, many semi-supervised learning algorithms have been developed for addressing the cross-domain text classiﬁcation problem by transferring knowledge across domains, and such algorithms include Transductive SVM (Joachims 1999), EM (Nigam et al. 2000), EM-based naive Bayes classiﬁer (Dai et al. ”
TrGraph: Cross-Network Transfer Learning via Common Signature Subgraphs (2015, background): “Transfer learning is a machine learning paradigm which aims at borrowing knowledge from a related domain to help build an accurate classifier in the target domain, where the labeled data in the target domain is very limited [9].”
Domain Adaptation with Coupled Subspaces (2011, background): “In many natural applications, though, we train our algorithm on a source distribution, but we desire high performance on target distributions which differ from that source [20, 32, 6, 28].”
Mining Aspect-Specific Opinion using a Holistic Lifelong Topic Model (2016, methodology): “Topic models have been used to help transfer learning in [48, 36, 20, 50].”
Feature-based transfer learning with real-world applications (2010, background): “Web applications of transfer learning include text classification across domains [151, 47, 48, 72, 195, 207, 36, 204, 213, 203], advertising [40, 39], learn to rank across domains [10, 192, 35, 68] and multi-domain collaborative filtering [103, 102, 29, 223, 143].”
Co-Training for Domain Adaptation (2011, background): “By allowing us to slowly change our training data from source to target, CODA has an advantage over representation-learning algorithms [6, 28], since they must decide a priori what the best representation is.”
Transfer learning using computational intelligence: A survey (2015, methodology): “[44] proposed a cross-domain text classification algorithm known as TPLSA to integrate labeled and unlabeled data from different but related domains.”
Co-Training for Cross-Lingual Sentiment Classification (2009, methodology): “, 2007a), Topic-bridged PLSA (Xue et al., 2008), Co-Clustering based classification (Dai et al.”
Survey : Domain Adaptation Algorithms for Natural Language Processing (2012, unknown): “The first class of algorithms we will discuss in this survey is based on feature transformation (Daumé III, 2007; Blitzer, McDonald, and Pereira, 2006; Jiang and Zhai, 2007b; Guo et al., 2009; Xue et al., 2008).”
Neural Structural Correspondence Learning for Domain Adaptation (2016, background): “…literature, including instance reweighting (Huang et al., 2007; Mansour et al., 2009), sub-sampling from both domains (Chen et al., 2011) and learning joint target and source feature representations (Blitzer et al., 2006; Daumé III, 2007; Xue et al., 2008; Glorot et al., 2011; Chen et al., 2012).”
Transfer Learning with Graph Co-Regularization (2012, background): “To facilitate transfer learning, we follow [8], [30], [31] and share the feature clusters across domains, i.”
Cross-Domain Recommendation via Cluster-Level Latent Factor Model (2013, background): “[18] have addressed the problem of using the auxiliary labeled data to help classify the unlabeled data in the target domain.”
Topic Modeling using Topics from Many Domains, Lifelong Learning and Big Data (2014, methodology): “Topic models have been used to help transfer learning (Xue et al., 2008; Pan & Yang, 2010).”