Domain adaptation with structural correspondence learning

Latent dirichlet allocation

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Latent Dirichlet Allocation

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A Maximum Entropy Model for Part-Of-Speech Tagging

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A review of single-source unsupervised domain adaptation.

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Simultaneous learning of pivots and representations for cross-domain sentiment classification

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Mismatched Training and Test Distributions Can Outperform Matched Ones

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Domain Adaptation for Authorship Attribution: Improved Structural Correspondence Learning

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PAC-Bayes and domain adaptation

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Domain Adaptation with Twin Support Vector Machines

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Structural Correspondence Learning for Cross-Lingual Sentiment Classification with One-to-Many Mappings

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Challenges and Recommended Solutions in Multi-Source and Multi-Domain Sentiment Analysis

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Atypical Sample Regularizer Autoencoder for Cross-Domain Human Activity Recognition

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Cross-domain sentiment classification based on key pivot and non-pivot extraction

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STranGAN: Adversarially-learnt Spatial Transformer for scalable human activity recognition

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Adapting Component Analysis

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Domain Adaptation for Coreference Resolution: An Adaptive Ensemble Approach

Yang, J. B., Mao, Q., Xiang, Q. L., Tsang, I. W.-H., Chai, K. M. A., & Chieu, H. L. (2012) · Cited by 18

Close Yet Distinctive Domain AdaptationPreprint

Luo, L., Wang, X., Hu, S., Wang, C., Tang, Y., & Chen, L. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1704.04235 · Cited by 18

Predictive Distribution Matching SVM for Multi-domain Learning

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Cross-domain neural-kernel networks

Mehrkanoon, S. (2019) · Pattern Recognition Letters · doi:10.1016/j.patrec.2019.06.007 · Cited by 17

Improving prediction for medical institution with limited patient data: Leveraging hospital-specific data based on multicenter collaborative research network

Li, J., Tian, Y., Li, R., Zhou, T., Li, J., Ding, K., & Li, J. (2021) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2021.102024 · Cited by 18

Topic Driven Adaptive Network for cross-domain sentiment classification

Zhu, Y., Qiu, Y., Wu, Q., Wang, F. L., & Rao, Y. (2022) · Information Processing & Management · doi:10.1016/j.ipm.2022.103230 · Cited by 18

Research on controllable deep learning of multi-channel image coding technology in Ferrographic Image fault classification

Xie, F., & Wei, H. (2022) · Tribology International · doi:10.1016/j.triboint.2022.107656 · Cited by 17

Cyber-Physical Systems Framework for Predictive Metrology in Semiconductor Manufacturing Process

Lee, J., Dong, H., Ji, D.-Y., & Kundu, P. (2023) · International Journal of Precision Engineering and Manufacturing-Smart Technology · doi:10.57062/ijpem-st.2022.0010 · Cited by 18

A Data Fusion Framework for Multi-Domain Morality Learning

Guo, S., Mokhberian, N., & Lerman, K. (2023) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v17i1.22145 · Cited by 20

Weighted SCL model for adaptation of sentiment classification

Tan, S., & Wang, Y. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.02.106 · Cited by 17

Importance weighting and unsupervised domain adaptation of POS taggers: a negative result

Plank, B., Johannsen, A., & Søgaard, A. (2014) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1104 · Cited by 18

Domain adaptation of information extraction models

Gupta, R., & Sarawagi, S. (2009) · ACM SIGMOD Record · doi:10.1145/1519103.1519109 · Cited by 17

Robust Domain Adaptation for Relation Extraction via Clustering Consistency

Nguyen, M. L., Tsang, I. W.-H., Chai, K. M. A., & Chieu, H. L. (2014) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.3115/v1/p14-1076 · Cited by 17

Low-Resource Cross-Domain Product Review Sentiment Classification Based on a CNN with an Auxiliary Large-Scale Corpus

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Transfer Learning for Unsupervised Influenza-like Illness Models from Online Search Data

Zou, B., Lampos, V., & Cox, I. J. (2019) · International World Wide Web Conference (WWW) · doi:10.1145/3308558.3313477 · Cited by 15

Cross-Domain Labeled LDA for Cross-Domain Text Classification

Jing, B., Lu, C., Wang, D., Zhuang, F., & Niu, C. (2018) · Proceedings · doi:10.1109/icdm.2018.00034 · Cited by 17

An Enhanced Approach to Map Domain-Specific Words in Cross-Domain Sentiment Analysis

Geethapriya, A., & Valli, S. (2021) · Information Systems Frontiers · doi:10.1007/s10796-020-10094-5 · Cited by 18

Domain-Shift Conditioning Using Adaptable Filtering Via Hierarchical Embeddings for Robust Chinese Spell Check

Nguyen, M., Ngo, G. H., & Chen, N. F. (2021) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2021.3083108 · Cited by 22

Meta-reweighted Regularization for Unsupervised Domain Adaptation

Li, S., Ma, W., Zhang, J., Liu, C. H., Liang, J., & Wang, G. (2021) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3114536 · Cited by 17

Relevance Ranking for Vertical Search Engines

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Collaborative Distribution Alignment for 2D image-based 3D shape retrieval

Hu, N., Zhou, H., Liu, A.-A., Huang, X., Zhang, S., Jin, G., Guo, J., & Li, X. (2022) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2021.103426 · Cited by 17

Quadruple Transfer Learning: Exploiting both shared and non-shared concepts for text classification

Pan, J., Hu, X., Zhang, Y., Li, P., Lin, Y., Li, H., He, W., & Li, L. (2015) · Knowledge-Based Systems · doi:10.1016/j.knosys.2015.09.017 · Cited by 17

Semi-supervised ranking for document retrieval

Duh, K., & Kirchhoff, K. (2010) · Computer Speech & Language · doi:10.1016/j.csl.2010.05.002 · Cited by 17

Domain Adaptation with Unlabeled Data for Dialog Act Tagging

Margolis, A., Livescu, K., & Mari Ostendorf, E. (2010) · Cited by 17

Transfer Learning Techniques

Weiss, K. R., Khoshgoftaar, T. M., & Wang, D. (2016) · Springer eBooks · doi:10.1007/978-3-319-44550-2_3 · Cited by 18

Knowledge Transfer Between Robots with Similar Dynamics for High-Accuracy Impromptu Trajectory Tracking

Zhou, S., Helwa, M. K., Schoellig, A. P., Sarabakha, A., & Kayacan, E. (2019) · European Control Conference (ECC) · doi:10.23919/ecc.2019.8796140 · Cited by 17

AugToAct

Faridee, A. Z. M., Khan, M. A. A. H., Pathak, N., & Roy, N. (2019) · EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous) · doi:10.1145/3360774.3360831 · Cited by 17

Domain Adaptation to Summarize Human Conversations

Sandu, O. A., Carenini, G., Murray, G., & Ng, R. T. (2010) · Cited by 17

Iterative parameter mixing for distributed large-margin training of structured predictors for natural language processing

Coppola, G. (2015) · ERA · Cited by 16

Towards a Speaker Independent Speech-BCI Using Speaker Adaptation

Dash, D., Wisler, A. A., Ferrari, P., & Wang, J. (2019) · Interspeech (USB) · doi:10.21437/interspeech.2019-3109 · Cited by 16

Mining Economic Sentiment Using Argumentation Structures

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A feature-free and parameter-light multi-task clustering framework

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Machine Translation Quality Estimation Across Domains

de Souza, J. G. C., Turchi, M., & Negri, M. (2014) · Cited by 16

Data-Driven Approaches for Paraphrasing across Language Variations.

Xu, W. H. (2014) · ProQuest LLC eBooks · Cited by 16

A General Regularization Framework for Domain Adaptation

Lu, W., Chieu, H. L., & Löfgren, J. (2016) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d16-1095 · Cited by 16

Clustered Model Adaption for Personalized Sentiment Analysis

Lin, G., Haines, B., & Wang, H. (2017) · International World Wide Web Conference (WWW) · doi:10.1145/3038912.3052693 · Cited by 16

Heterogeneous transfer learning techniques for machine learning

Iqbal, M. S., Luo, B., Khan, T., Mehmood, R., & Sadiq, M. (2018) · Iran Journal of Computer Science · doi:10.1007/s42044-017-0004-z · Cited by 16

Experiments with Convolutional Neural Networks for Multi-Label Authorship Attribution

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HALO: Hardware-Aware Learning to Optimize

Li, C., Chen, T., You, H., Wang, Z., & Lin, Y. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58545-7_29 · Cited by 16

Domain Adaptation Transfer Extreme Learning Machines

Lei Zhang, A., & Zhang, D. D. (2014) · Proceedings in adaptation, learning and optimization · doi:10.1007/978-3-319-14063-6_10 · Cited by 16

Domain-Adaptive Text Classification with Structured Knowledge from Unlabeled Data

Li, T., Chen, X., Dong, Z., Keutzer, K., & Zhang, S. (2022) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2022/585 · Cited by 17

Language Models as Representations for Weakly-Supervised NLP Tasks

Huang, F., Yates, E., Ahuja, A., & Downey, D. (2012) · Cited by 16

Transductive Domain Adaptation with Affinity Learning

Shu, L., & Latecki, L. J. (2015) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/2806416.2806643 · Cited by 16

Analyzing Urdu Social Media for Sentiments using Transfer Learning with Controlled Translations

Mukund, S., & Srihari, R. K. (2012) · Cited by 16

Resolving Ambiguity in Sentiment Classification

Deng, S., Sinha, A. P., & Zhao, H. (2017) · ACM Transactions on Management Information Systems · doi:10.1145/3046684 · Cited by 16

Improving SCL model for sentiment-transfer learning

Tan, S., & Cheng, X. (2009) · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT) · doi:10.3115/1620853.1620903 · Cited by 16

Transfer Learning-Based Dual GCN for Radar Active Deceptive Jamming Few-Shot Recognition

Wu, Z., Wang, T., Cao, Y., Zhang, M., Guo, W., & Yang, L. (2024) · IEEE Transactions on Aerospace and Electronic Systems · doi:10.1109/taes.2024.3464565 · Cited by 16

Multichannel LSTM-CRF for Named Entity Recognition in Chinese Social Media

Dong, C., Wu, H., Zhang, J., & Zong, C. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-69005-6_17 · Cited by 15

Domain adaptation with a shrinkable discrepancy strategy for cross-domain sentiment classification

Fu, Y., & Liu, Y. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.04.092 · Cited by 15

Unsupervised Multitarget Domain Adaptation With Dictionary-Bridged Knowledge Exploitation

Tian, Q., Ma, C., Cao, M., Wan, J., Lei, Z., & Chen, S. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3193289 · Cited by 15

Out-of-Distribution Generalization in Natural Language Processing: Past, Present, and Future

Yang, L., Song, Y., Ren, X., Lyu, C., Wang, Y., Zhuo, J., Liu, L., Wang, J., Foster, J., & Zhang, Y. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.276 · Cited by 21

Healing Sample Selection Bias by Source Classifier Selection

Seah, C.-W., Tsang, I. W.-H., & Ong, Y.-S. (2011) · Proceedings · doi:10.1109/icdm.2011.73 · Cited by 15

Domain Adaptive Classification

Mirrashed, F., & Rastegari, M. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.324 · Cited by 15

Long term bank failure prediction using Fuzzy Refinement-based Transductive Transfer learning

Behbood, V., Jie Lu, B., & Zhang, G. (2011) · IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzzy.2011.6007633 · Cited by 15

Learning Domain-specific Sentiment Lexicon with Supervised Sentiment-aware LDA

Yang, M., Zhu, D., Mustafa, R., & Chow, K.-P. (2014) · Frontiers in artificial intelligence and applications · doi:10.3233/978-1-61499-419-0-927 · Cited by 15

Transfer Learning for Wearable Long-Term Social Speech Evaluations

Chen, Y., Gao, B., Jiang, L., Yin, K., Gu, J. J., & Woo, W. L. (2018) · IEEE Access · doi:10.1109/access.2018.2876122 · Cited by 15

Cross-species Data Classification by Domain Adaptation via Discriminative Heterogeneous Maximum Mean Discrepancy

Li, L., & Cai, M. (2019) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2019.2914103 · Cited by 15

Towards Visual Feature Translation

Hu, J., Ji, R., Liu, H., Zhang, S., Deng, C. D., & Tian, Q. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00312 · Cited by 14

The application of Deep Learning in Persian Documents Sentiment Analysis

Dastgheib, M. B., Koleini, S., & Rasti, F. (2020) · DOAJ (DOAJ: Directory of Open Access Journals) · Cited by 15

Unsupervised Domain Adaptation for Acoustic Scene Classification Using Band-Wise Statistics Matching

Mezza, A. I., Habets, E. A. P., Müller, M., & Sarti, A. (2020) · European Signal Processing Conference (EUSIPCO) · doi:10.23919/eusipco47968.2020.9287533 · Cited by 15

Semi-supervised Discourse Relation Classification with Structural Learning

Hernault, H., Bollegala, D., & Ishizuka, M. (2011) · Lecture notes in computer science · doi:10.1007/978-3-642-19400-9_27 · Cited by 15

ClassiNet -- Predicting Missing Features for Short-Text Classification

Bollegala, D., Atanasov, V., Maehara, T., & Kawarabayashi, K. (2018) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3201578 · Cited by 19

CCHAN: An End to End Model for Cross Domain Sentiment Classification

Tu, M., & Zhao, X. (2019) · IEEE Access · doi:10.1109/access.2019.2910300 · Cited by 15

Collaborative learning based on centroid-distance-vector for wearable devices

Kong, D., Bao, Y., & Chen, W. (2020) · Knowledge-Based Systems · doi:10.1016/j.knosys.2020.105569 · Cited by 15

Cross-Lingual Knowledge Transferring by Structural Correspondence and Space Transfer

Wang, D., Wu, J., Yang, J., Jing, B., Zhang, W., He, X., & Zhang, H. (2021) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2021.3051005 · Cited by 15

Semiautomatic Construction of Cross-Period Thesaurus

Liebeskind, C., Dagan, I., & Schler, J. (2016) · Journal on Computing and Cultural Heritage · doi:10.1145/2994151 · Cited by 15

Detecting dementia from written and spoken language

Masrani, V. (2018) · cIRcle (University of British Columbia) · doi:10.14288/1.0362923 · Cited by 18

Financial Aspect-Based Sentiment Analysis using Deep RepresentationsPreprint

Yang, S. Y., Rosenfeld, J., & Makutonin, J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.07931 · Cited by 15

GearNet: Stepwise Dual Learning for Weakly Supervised Domain Adaptation

Xie, R., Wei, H., Feng, L., & An, B. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i8.20851 · Cited by 14

Contrasting augmented features for domain adaptation with limited target domain data

Yu, X., Gu, X., & Sun, J. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.110145 · Cited by 15

Heterogeneous transfer learning: recent developments, applications, and challenges

Khan, S. A., Yin, P., Guo, Y., Asim, M., & El‐Latif, A. A. A. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-18352-3 · Cited by 14

Marginalised Stacked Denoising Autoencoders for Robust Representation of Real-Time Multi-View Action Recognition

Gu, F., Flórez‐Revuelta, F., Monekosso, D., & Remagnino, P. (2015) · Sensors · doi:10.3390/s150717209 · Cited by 14

Mining employment market via text block detection and adaptive cross-domain information extraction

Wong, T.-L., Lam, W. P., & Chen, B. (2009) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/1571941.1571991 · Cited by 14

Sentiment detection with auxiliary data

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Model Adaptation for Personalized Opinion Analysis

Boni, M. A., Zhou, K., Wang, H., & Gerber, M. S. (2015) · doi:10.3115/v1/p15-2126 · Cited by 14

Domain Adaptation and Instance Selection for Disease Syndrome Classification over Veterinary Clinical Notes

Hur, B., Baldwin, T., Verspoor, K. M., Hardefeldt, L. Y., & Gilkerson, J. R. (2020) · Workshop on Biomedical Language Processing (BioNLP) · doi:10.18653/v1/2020.bionlp-1.17 · Cited by 15

Unsupervised Domain Adaptation in Cross-corpora Abusive Language Detection

Bose, T., Illina, I., & Fohr, D. (2021) · International Workshop on Natural Language Processing for Social Media (SocialNLP) · doi:10.18653/v1/2021.socialnlp-1.10 · Cited by 14

Multi-Domain Adaptation for SMT Using Multi-Task Learning

Cui, L., Chen, X., Zhang, D., Liu, S., Li, M., & Zhou, M. Q. (2013) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d13-1107 · Cited by 14

Cross-Language Speech Emotion Recognition Using Bag-of-Word Representations, Domain Adaptation, and Data Augmentation

Kshirsagar, S., & Falk, T. H. (2022) · Sensors · doi:10.3390/s22176445 · Cited by 16

Improved Feature Representation Using Collaborative Network for Cross-Domain Sentiment Analysis

Gunasekar, M., & Thilagamani, S. (2023) · Information Technology And Control · doi:10.5755/j01.itc.52.1.32119 · Cited by 14

Domain adaptive object detection

Mirrashed, F., Morariu, V. I., Siddiquie, B., Feris, R. S., & Davis, L. S. (2013) · Proceedings · doi:10.1109/wacv.2013.6475036 · Cited by 14

Knowledge transfer based on feature representation mapping for text classification

Meng, J., Lin, H., & Li, Y. (2011) · Expert Systems with Applications · doi:10.1016/j.eswa.2011.02.085 · Cited by 14

Grammar-Driven versus Data-Driven: Which Parsing System Is More Affected by Domain Shifts?

Plank, B., & van Noord, G. (2010) · Cited by 14

Multi-Instance Learning with Distribution Change

Zhang, W., & Zhou, Z. (2014) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v28i1.8971 · Cited by 13

Adapting pedestrian detectors to new domains: A comprehensive review

Htike, K. K., & Hogg, D. (2016) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2016.01.029 · Cited by 14

Improving sentiment analysis performance on morphologically rich languages: Language and domain independent approach

Kincl, T., Novák, M., & Přibil, J. (2019) · Computer Speech & Language · doi:10.1016/j.csl.2019.01.001 · Cited by 15

Finding Answers from the Word of God: Domain Adaptation for Neural Networks in Biblical Question Answering

Zhao, H. J., & Liu, J. (2018) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2018.8489756 · Cited by 16

Transfer Learning via Multi-View Principal Component Analysis

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Domain adaptation of natural language processing systems

Pereira, F. M. B., & Blitzer, J. C. (2008) · Cited by 58

Neural Structural Correspondence Learning for Domain Adaptation

Ziser, Y., & Reichart, R. (2017) · Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017) · doi:10.18653/v1/k17-1040 · Cited by 122

Learning Structural Correspondences Across Different Linguistic Domains with Synchronous Neural Language Models

Gouws, S., Rooyen, G. V., & Bengio, Y. (2013) · Cited by 1

Domain Adaptation for Authorship Attribution: Improved Structural Correspondence Learning

Sapkota, U., Solorio, T., Montes, M., & Bethard, S. J. (2016) · Proceedings of the conference - Association for Computational Linguistics. Meeting · doi:10.18653/v1/p16-1210 · Cited by 18

Cross-Lingual Adaptation using Structural Correspondence LearningPreprint

Prettenhofer, P., & Stein, B. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1008.0716