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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.

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 R&D Team’s Research Paper Titled “Towards Weakly-Supervised Hate Speech Classification Across Datasets” Was Accepted to the 7th Workshop on Online Abuse and Harms (WOAH)
June 6, 2023
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

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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