Cross-Language Text Classification Using Structural Correspondence Learning

By Peter Prettenhofer, Benno Stein. Annual Meeting of the Association for Computational Linguistics, 2010

338 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/fb3054bcaf1b6de06979cc50ea51d3f5e3560a19

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Citations in the corpus

Cross Language Text Classification via Subspace Co-regularized Multi-view Learning (2012, methodology): “…language text classiﬁcation accuracy, including an information bottleneck method (Ling et al., 2008), EM-based model translation techniques (Shi et al., 2010; Rigutini & Maggini, 2005), and cross language domain adaptation methods (Wei & Pal, 2010; Prettenhofer & Stein, 2010; Wan et al., 2011).”
Zero-Shot Cross-lingual Classification Using Multilingual Neural Machine Translation (2018, background): “Following Prettenhofer and Stein (2010), we mapped the reviews with lower scores (1 and 2) to negative examples and the reviews with higher scores (4 and 5) to positive examples, thereby turning it into a binary classiﬁcation problem.”
Multi-View Clustering and Feature Learning via Structured Sparsity (2013, methodology): “With the advances of machine translation techniques, one can easily get different translations for one document (Prettenhofer & Stein, 2010), and the translation in each language can be considered as a view.”
A Survey of Cross-lingual Word Embedding Models (2017, background): “Mogadala and Rettinger (2016) evaluate their embeddings on the multilingual Amazon product review dataset of Prettenhofer and Stein (2010).”
Transfer Neural Trees for Heterogeneous Domain Adaptation (2016, background): “, cross-domain object recognition [2–5] and crosslanguage text categorization [6, 7]) can be viewed as DA problems.”
Domain Adaptation for Visual Applications: A Comprehensive Survey (2017, background): “classification [9, 10, 11], sentiment analysis [12, 13], WiFi-based localization [14], speech recognition across different speakers [15, 16], object recognition in images acquired in different conditions [17, 18, 19, 20, 21], video concept detection [22], video event recognition [23], activity recog”
How Transferable are Neural Networks in NLP Applications? (2016, methodology): “Domain—in the sense we use throughout this paper—is defined by datasets. al., 2010), instance weighting (Jiang and Zhai, 2007; Foster et al., 2010), and structural correspondence learning (Blitzer et al., 2006; Prettenhofer and Stein, 2010).”
Bridging the domain gap in cross-lingual document classification (2019, background): “The subsequent work (Prettenhofer & Stein, 2010) proposes the cross-lingual sentiment classification datasets, and (Lewis et al., 2004; Klementiev et al., 2012; Schwenk & Li, 2018) have extended this to the news domain.”
Deep Asymmetric Transfer Network for Unbalanced Domain Adaptation (2018, background): “For AMAZON REVIEWS, we all use 3-layer deep models for two domains.”
Heterogeneous Domain Adaptation for Multiple Classes (2014, background): “Formally, one can learn two feature mappings P and Q to transform the source domain data XS and target domain data XT to a new latent feature space such that the difference between the mapped domain data PXS and QXT is reduced [29, 26, 31, 15].”
Bilingual Word Embeddings from Parallel and Non-parallel Corpora for Cross-Language Text Classification (2016, methodology): “While for label-aligned non-parallel document corpora, only training and testing collections of the cross-language multi-domain Amazon product reviews(CL-APR) (Prettenhofer and Stein, 2010) corpus with sentiment labels is used.”
Cross-language web page classification via dual knowledge transfer using nonnegative matrix tri-factorization (2011, methodology): “1 Challenges in Cross-Language Web Page Classification and Our Motivations One of the most widely used strategy in cross-language Web page (text) classification is using language translation [15, 16, 18–21, 23].”
FlauBERT: Unsupervised Language Model Pre-training for French (2019, background): “CLS The Cross Lingual Sentiment CLS (Prettenhofer and Stein, 2010) dataset consists of Amazon reviews for three product categories: books, DVD, and music in four languages: English, French, German, and Japanese.”
Bilingual Co-Training for Sentiment Classification of Chinese Product Reviews (2011, unknown): “…the problem—for example, the EM-based algorithm (Rigutini, Maggini, and Liu 2005), the information bottleneck approach (Ling et al. 2008), multilingual domain models (Gliozzo and Strapparava 2005), and the structural correspondence learning approach (Prettenhofer and Stein 2010; Wei and Pal 2010).”
Hybrid Heterogeneous Transfer Learning through Deep Learning (2014, background): “Specifically, one can learn two different feature mappings to transform the source domain and target domain data to a latent feature space respectively (Shi et al. 2010; Prettenhofer and Stein 2010; Wang and Mahadevan 2011; Duan, Xu, and Tsang 2012).”
Cross-Lingual Sentiment Classification with Bilingual Document Representation Learning (2016, methodology): “We directly use the experimental results reported in (Prettenhofer and Stein, 2010).”