Hanshi Wang


2016

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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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Anecdote Recognition and Recommendation
Wei Song | Ruiji Fu | Lizhen Liu | Hanshi Wang | Ting Liu
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers

We introduce a novel task Anecdote Recognition and Recommendation. An anecdote is a story with a point revealing account of an individual person. Recommending proper anecdotes can be used as evidence to support argumentative writing or as a clue for further reading. We represent an anecdote as a structured tuple — < person, story, implication >. Anecdote recognition runs on archived argumentative essays. We extract narratives containing events of a person as the anecdote story. More importantly, we uncover the anecdote implication, which reveals the meaning and topic of an anecdote. Our approach depends on discourse role identification. Discourse roles such as thesis, main ideas and support help us locate stories and their implications in essays. The experiments show that informative and interpretable anecdotes can be recognized. These anecdotes are used for anecdote recommendation. The anecdote recommender can recommend proper anecdotes in response to given topics. The anecdote implication contributes most for bridging user interested topics and relevant anecdotes.

2015

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The Discovery of Natural Typing Annotations: User-produced Potential Chinese Word Delimiters
Dakui Zhang | Yu Mao | Yang Liu | Hanshi Wang | Chuyuan Wei | Shiping Tang
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

2011

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A New Unsupervised Approach to Word Segmentation
Hanshi Wang | Jian Zhu | Shiping Tang | Xiaozhong Fan
Computational Linguistics, Volume 37, Issue 3 - September 2011