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Changsheng Liu,  刘昌盛
PhD student 
Computer Science Department
University of Pittsburgh






I am a PhD candidate in the computer science department, University of Pittsburgh.  My advisor is Prof. Rebecca Hwa.

Research Interests
I am interested in Machine Learning and Artificial Intelligence in general. My major research work is Natural Language Processing. Specifically, I am interested in semantics of word, phrase or text.

Publication
  • Changsheng Liu and Rebecca Hwa. "Heuristically Informed Unsupervised Idiom Usage Recognition", In The Proceedings of Empirical Methods in Natural Language Processing (EMNLP), Brussels, Belgium, 2018, (Long paper).
  • Changsheng Liu and Rebecca Hwa. "Representations of Context in Recognizing the Figurative and Literal Usages of Idioms." In The Proceedings of Thirty-First AAAI Conference on Artificial Intelligence (AAAI), San Francisco, California, 2017, (Long paper).
  • Changsheng Liu and Rebecca Hwa. "Phrasal Substitution of Idiomatic Expressions." In Proceedings of the 15th Annual Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL), 2016, (Long paper).
  • Yanzhen Zou, Changsheng Liu, Yong Jin, and Bing Xie. "Assessing software quality through web comment search and analysis." In International Conference on Software Reuse, pp. 208-223. Springer, Berlin, Heidelberg, 2013, (Long paper).
  • Changsheng Liu, Yanzhen Zou, Sibo Cai, Bing Xie, and Hong Mei. "Finding the merits and drawbacks of software resources from comments." In Proceedings of the 2011 26th IEEE/ACM International Conference on Automated Software Engineering (ASE, Short Paper), pp. 432-435. IEEE Computer Society, 2011, (Short paper).
Invited Talk
  • Phrasal Substitution of Idiomatic Expressions @turnitin, Pittsburgh, June 2016

Course
  • Artificial Intelligence
  • NLP
  • Machine Learning
  • Algorithm
  • Operating System

Teaching Assistant
  • Intro to Artificial Intelligence
  • Machine Learning
  • Natural Language Processing
  • Discrete Structure 
  • Python