Gao Cong


A Novel Cascade Model for Learning Latent Similarity from Heterogeneous Sequential Data of MOOC
Zhuoxuan Jiang | Shanshan Feng | Gao Cong | Chunyan Miao | Xiaoming Li
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Recent years have witnessed the proliferation of Massive Open Online Courses (MOOCs). With massive learners being offered MOOCs, there is a demand that the forum contents within MOOCs need to be classified in order to facilitate both learners and instructors. Therefore we investigate a significant application, which is to associate forum threads to subtitles of video clips. This task can be regarded as a document ranking problem, and the key is how to learn a distinguishable text representation from word sequences and learners’ behavior sequences. In this paper, we propose a novel cascade model, which can capture both the latent semantics and latent similarity by modeling MOOC data. Experimental results on two real-world datasets demonstrate that our textual representation outperforms state-of-the-art unsupervised counterparts for the application.


Using Conditional Random Fields to Extract Contexts and Answers of Questions from Online Forums
Shilin Ding | Gao Cong | Chin-Yew Lin | Xiaoyan Zhu
Proceedings of ACL-08: HLT


Detecting Erroneous Sentences using Automatically Mined Sequential Patterns
Guihua Sun | Xiaohua Liu | Gao Cong | Ming Zhou | Zhongyang Xiong | John Lee | Chin-Yew Lin
Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics