Tong Liu


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Learning to Identify Sentence Parallelism in Student Essays
Wei Song | Tong Liu | Ruiji Fu | Lizhen Liu | Hanshi Wang | Ting Liu
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

Parallelism is an important rhetorical device. We propose a machine learning approach for automated sentence parallelism identification in student essays. We build an essay dataset with sentence level parallelism annotated. We derive features by combining generalized word alignment strategies and the alignment measures between word sequences. The experimental results show that sentence parallelism can be effectively identified with a F1 score of 82% at pair-wise level and 72% at parallelism chunk level. Based on this approach, we automatically identify sentence parallelism in more than 2000 student essays and study the correlation between the use of sentence parallelism and the types and quality of essays.

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Understanding Discourse on Work and Job-Related Well-Being in Public Social Media
Tong Liu | Christopher Homan | Cecilia Ovesdotter Alm | Megan Lytle | Ann Marie White | Henry Kautz
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)


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Toward Macro-Insights for Suicide Prevention: Analyzing Fine-Grained Distress at Scale
Christopher Homan | Ravdeep Johar | Tong Liu | Megan Lytle | Vincent Silenzio | Cecilia Ovesdotter Alm
Proceedings of the Workshop on Computational Linguistics and Clinical Psychology: From Linguistic Signal to Clinical Reality