Cool English: a Grammatical Error Correction System Based on Large Learner Corpora
Yu-Chun Lo | Jhih-Jie Chen | Chingyu Yang | Jason Chang
Proceedings of the 27th International Conference on Computational Linguistics: System Demonstrations
This paper presents a grammatical error correction (GEC) system that provides corrective feedback for essays. We apply the sequence-to-sequence model, which is frequently used in machine translation and text summarization, to this GEC task. The model is trained by EF-Cambridge Open Language Database (EFCAMDAT), a large learner corpus annotated with grammatical errors and corrections. Evaluation shows that our system achieves competitive performance on a number of publicly available testsets.
Sensing Emotions in Text Messages: An Application and Deployment Study of EmotionPush
Shih-Ming Wang | Chun-Hui Scott Lee | Yu-Chun Lo | Ting-Hao Huang | Lun-Wei Ku
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations
Instant messaging and push notifications play important roles in modern digital life. To enable robust sense-making and rich context awareness in computer mediated communications, we introduce EmotionPush, a system that automatically conveys the emotion of received text with a colored push notification on mobile devices. EmotionPush is powered by state-of-the-art emotion classifiers and is deployed for Facebook Messenger clients on Android. The study showed that the system is able to help users prioritize interactions.
- Jhih-Jie Chen 1
- Chingyu Yang 1
- Jason S. Chang 1
- Shih-Ming Wang 1
- Chun-Hui Scott Lee 1
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