Yan Liang

Affiliations:
  • Amazon Product Graph Team, USA
  • University of Oklahoma, Norman, OK, USA (PhD 2022)


According to our database1, Yan Liang authored at least 11 papers between 2017 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Bibliography

2023
Concept2Box: Joint Geometric Embeddings for Learning Two-View Knowledge Graphs.
Proceedings of the Findings of the Association for Computational Linguistics: ACL 2023, 2023

2022
Ask-and-Verify: Span Candidate Generation and Verification for Attribute Value Extraction.
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing: EMNLP 2022 - Industry Track, Abu Dhabi, UAE, December 7, 2022

TAED: Topic-Aware Event Detection.
Proceedings of the IEEE International Conference on Big Data, 2022

2021
All You Need to Know to Build a Product Knowledge Graph.
Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

PAM: Understanding Product Images in Cross Product Category Attribute Extraction.
Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

AdaTag: Multi-Attribute Value Extraction from Product Profiles with Adaptive Decoding.
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, 2021

2020
AutoKnow: Self-Driving Knowledge Collection for Products of Thousands of Types.
Proceedings of the KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2020

2018
New Techniques for Coding Political Events across Languages.
Proceedings of the 2018 IEEE International Conference on Information Reuse and Integration, 2018

2017
Formalizing Interruptible Algorithms for Human over-the-loop Analytics.
CoRR, 2017

Adaptive scalable pipelines for political event data generation.
Proceedings of the 2017 IEEE International Conference on Big Data (IEEE BigData 2017), 2017

[Research paper] formalizing interruptible algorithms for human over-the-loop analytics.
Proceedings of the 2017 IEEE International Conference on Big Data (IEEE BigData 2017), 2017


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