RESEARCH & PROTOTYPING

Innovation for Smarter Automation

We build AI-driven advertising technologies and solutions for marketers.

PARTNER INSTITUTES

Knorex R&D members come from these world-leading universities, institutes, and organizations in AI and NLP.

Knorex Research and Prototyping Labs | Delivering Cutting-Edge Innovations 1

Ho Chi Minh University of Technology

Knorex Research and Prototyping Labs | Delivering Cutting-Edge Innovations 2

Foreign Trade University, Hanoi

Korea Advanced Institute of Science & Technology (KAIST)

Knorex Research and Prototyping Labs | Delivering Cutting-Edge Innovations 4

Vietnam National University (VNU)

Knorex Research and Prototyping Labs | Delivering Cutting-Edge Innovations 5

Ho Chi Minh City University of Science

Knorex Research and Prototyping Labs | Delivering Cutting-Edge Innovations 6

VietAI

Knorex Research and Prototyping Labs | Delivering Cutting-Edge Innovations 7

National University of Singapore (NUS)

Knorex Research and Prototyping Labs | Delivering Cutting-Edge Innovations 8

Royal Melbourne Institute of Technology (RMIT), Vietnam

Knorex Research and Prototyping Labs | Delivering Cutting-Edge Innovations 9

Chulalongkorn University

Knorex Research and Prototyping Labs | Delivering Cutting-Edge Innovations 10

Nanyang Technological University (NTU)

Knorex Research and Prototyping Labs | Delivering Cutting-Edge Innovations 11

University Pompeu Fabra (UPF)

We have intern and research stay positions available all year round. Please email hr@knorex.com to apply.

DELIVERING WORLD-CLASS INNOVATION

Latest Updates

Knorex Presents at the 15th International Natural Language Generation Conference

July 24, 2022

Knorex Research Paper on AI-Powered Slogan Generation Published in Natural Language Engineering (NLE)

February 7, 2022

Knorex Unveils its Machine Learning Engine KAIROS to Power Universal Advertising in Knorex XPO

June 22, 2020

Knorex Research and Prototyping Labs | Delivering Cutting-Edge Innovations 12

Knorex R&D Team Presenting At The NAACL 2021 Conference

April 26, 2021

Knorex research scientists Yiping Jin and Vishakha Kadam will be presenting their team’s research papers titled “Bootstrapping Large-Scale Fine-Grained Contextual Advertising Classifier from Wikipedia” and

Knorex Unveils its Machine Learning Engine KAIROS to Power Universal Marketing in Knorex XPO

June 22, 2020

SUNNYVALE, CA, June 25, 2020 – Knorex, the leading provider of cross-channel marketing cloud platform, today announced the launch of its Machine Learning (ML) engine,

Knorex R&D Team Publishes Research Paper on Data-less Machine Learning Algorithm for Text Classification

June 22, 2020

We are thrilled to announce that our Research & Prototyping Team’s (R&P) research paper relating to contextual targeting titled “Learning from noisy out-of-domain corpus using

Publications

Jin, Y., Wanner, L., Kadam, V., & Shvet, A. (2023). Towards Weakly-Supervised Hate Speech Classification Across Datasets. Proceedings of the 7th Workshop on Online Abuse and Harms (WOAH 2023). Toronto, Canada.

Jin, Y., Kadam, V., & Wanvarie, D. (2022). Plot Writing From Pre-Trained Language Models. Proceedings of the 15th International Conference on Natural Language Generation (INLG). Maine, USA.

Kadam, V., Jin, Y., & Nguyen-Hoang, B. D. (2022). Automated Ad Creative Generation. Proceedings of the 15th International Conference on Natural Language Generation (INLG). Maine, USA.

Jin, Y., Knorex Pte Ltd, 2021. Cross-domain contextual targeting without any in-domain labelled data. U.S. Patent 11,093,969.

Jin, Y., Bhatia, A., & Wanvarie, D. (2021, June). Seed Word Selection for Weakly-Supervised Text Classification with Unsupervised Error Estimation. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Student Research Workshop (pp. 112-118).

Jin, Y., Kadam, V., & Wanvarie, D. (2021, June). Bootstrapping Large-Scale Fine-Grained Contextual Advertising Classifier from Wikipedia. In Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15) (pp. 1-9).

Jin, Y., Bhatia, A., Wanvarie, D., & Le, P. T. (In press). Towards Improving Coherence and Diversity of Slogan Generation. Natural Language Engineering, 1, 33.

Jin, Y., Wanvarie, D., & Le, P. T. (2022). Learning from noisy out-of-domain corpus using dataless classification. Natural Language Engineering, 28(1), 39-69.

Charoenphakdee, N., Lee, J., Jin, Y., Wanvarie, D., & Sugiyama, M. (2019, November). Learning Only from Relevant Keywords and Unlabeled Documents. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) (pp. 3993-4002).

Jin, Y., Wanvarie, D., & Le, P. T. (2019, April). Bridging the Gap Between Research and Production with CODE. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 277-288). Springer, Cham.

Nguyen-Hoang, B. D., Pham-Hong, B. T., Jin, Y., & Le, P. T. (2018). Genre-oriented web content extraction with deep convolutional neural networks and statistical methods. In Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation.

Jin, Y., Wanvarie, D., & Le, P. (2017, November). Combining lightly-supervised text classification models for accurate contextual advertising. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (pp. 545-554).

Jin, Y., & Le, P. (2016). Selecting domain-specific concepts for question generation with lightly-supervised methods. In Proceedings of the 9th International Natural Language Generation Conference (pp. 133-142).

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RESEARCH & PROTOTYPING

Innovation for Smarter Automation

PARTNER INSTITUTES

Ho Chi Minh University of Technology

Foreign Trade University, Hanoi

Korea Advanced Institute of Science & Technology (KAIST)

Vietnam National University (VNU)

Ho Chi Minh City University of Science

VietAI

National University of Singapore (NUS)

Royal Melbourne Institute of Technology (RMIT), Vietnam

Chulalongkorn University

Nanyang Technological University (NTU)

University Pompeu Fabra (UPF)

DELIVERING WORLD-CLASS INNOVATION

Latest Updates

Knorex R&D Team Presenting At The NAACL 2021 Conference

Knorex Unveils its Machine Learning Engine KAIROS to Power Universal Marketing in Knorex XPO

Knorex R&D Team Publishes Research Paper on Data-less Machine Learning Algorithm for Text Classification

Publications

Jin, Y., Wanner, L., Kadam, V., & Shvet, A. (2023). Towards Weakly-Supervised Hate Speech Classification Across Datasets. Proceedings of the 7th Workshop on Online Abuse and Harms (WOAH 2023). Toronto, Canada.

Jin, Y., Kadam, V., & Wanvarie, D. (2022). Plot Writing From Pre-Trained Language Models. Proceedings of the 15th International Conference on Natural Language Generation (INLG). Maine, USA.

Kadam, V., Jin, Y., & Nguyen-Hoang, B. D. (2022). Automated Ad Creative Generation. Proceedings of the 15th International Conference on Natural Language Generation (INLG). Maine, USA.

Jin, Y., Knorex Pte Ltd, 2021. Cross-domain contextual targeting without any in-domain labelled data. U.S. Patent 11,093,969.

Jin, Y., Kadam, V., & Wanvarie, D. (2021, June). Bootstrapping Large-Scale Fine-Grained Contextual Advertising Classifier from Wikipedia. In Proceedings of the Fifteenth Workshop on Graph-Based Methods for Natural Language Processing (TextGraphs-15) (pp. 1-9).

Jin, Y., Bhatia, A., Wanvarie, D., & Le, P. T. (In press). Towards Improving Coherence and Diversity of Slogan Generation. Natural Language Engineering, 1, 33.

Jin, Y., Wanvarie, D., & Le, P. T. (2022). Learning from noisy out-of-domain corpus using dataless classification. Natural Language Engineering, 28(1), 39-69.

Jin, Y., Wanvarie, D., & Le, P. T. (2019, April). Bridging the Gap Between Research and Production with CODE. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 277-288). Springer, Cham.​

Nguyen-Hoang, B. D., Pham-Hong, B. T., Jin, Y., & Le, P. T. (2018). Genre-oriented web content extraction with deep convolutional neural networks and statistical methods. In Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation.​

Jin, Y., Wanvarie, D., & Le, P. (2017, November). Combining lightly-supervised text classification models for accurate contextual advertising. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers) (pp. 545-554).

Jin, Y., & Le, P. (2016). Selecting domain-specific concepts for question generation with lightly-supervised methods. In Proceedings of the 9th International Natural Language Generation Conference (pp. 133-142).​

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Jin, Y., Wanvarie, D., & Le, P. T. (2019, April). Bridging the Gap Between Research and Production with CODE. In Pacific-Asia Conference on Knowledge Discovery and Data Mining (pp. 277-288). Springer, Cham.

Nguyen-Hoang, B. D., Pham-Hong, B. T., Jin, Y., & Le, P. T. (2018). Genre-oriented web content extraction with deep convolutional neural networks and statistical methods. In Proceedings of the 32nd Pacific Asia Conference on Language, Information and Computation.

Jin, Y., & Le, P. (2016). Selecting domain-specific concepts for question generation with lightly-supervised methods. In Proceedings of the 9th International Natural Language Generation Conference (pp. 133-142).