Web IR / NLP Group (WING) @ NUS

The Web, Information Retrieval / Natural Language Processing Group (WING) explores the research area of applied language processing and information retrieval to the Web and related technologies. Areas of current interest are question answering, scholarly digital libraries, verb similarity, focused crawling, citation parsing and spidering, web page classification and division, text segmentation, and full text analysis. WING is headed by Min (A/P Min-Yen KAN). We are based in the Computational Linguistics Laboratory of the School of Computing at the National University of Singapore. We often work with the Natural Language Processing Group and the Lab for Media Search. We are part of the Media Technologies research group umbrella.

Latest News

CIVIC-AI: Stanford's SALT Lab and WING.NUS Collaborate on Social Intelligence and the Future of AI and Work
CIVIC-AI: Stanford's SALT Lab and WING.NUS Collaborate on Social Intelligence and the Future of AI and Work

The inaugural CIVIC-AI workshop (13–14 July 2026) kicks off a major collaboration between Stanford’s SALT Lab and WING.NUS, bringing together researchers, policymakers, and funding agencies to examine the Social Intelligence of Foundation Models and the Future of AI and Work.

WING is looking for postdoctoral scholars and doctoral students!
WING is looking for postdoctoral scholars and doctoral students!

The Web, Information Retrieval, and Natural Language Processing (WING) group at the School of Computing, National University of Singapore is actively seeking postdoctoral scholars and doctoral students to start in 2026 across three cutting-edge research areas: AI for Science, LLM Socioethical Alignment, and Fact Checking. WING is led by Associate Professor Kan Min-Yen.

Latest Conference Publications

(2026). Bridging Talk and Thought: Understanding Dialogue Dynamics Across Collaborative Problem-Solving Contexts. Proceedings of the 27th Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL 2026).

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(2026). Beyond the Crowd: LLM-Augmented Community Notes for Governing Health Misinformation. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

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(2026). What's Left Unsaid? Detecting and Correcting Misleading Omissions in Multimodal News Previews. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

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(2026). When Does Mixing Help? Analyzing Query Embedding Interpolation in Multilingual Dense Retrieval. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

PDF Cite DOI ACL Anthology

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