Fang Liu


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Ling@CASS Solution to the NLP-TEA CGED Shared Task 2018
Qinan Hu | Yongwei Zhang | Fang Liu | Yueguo Gu
Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications

In this study, we employ the sequence to sequence learning to model the task of grammar error correction. The system takes potentially erroneous sentences as inputs, and outputs correct sentences. To breakthrough the bottlenecks of very limited size of manually labeled data, we adopt a semi-supervised approach. Specifically, we adapt correct sentences written by native Chinese speakers to generate pseudo grammatical errors made by learners of Chinese as a second language. We use the pseudo data to pre-train the model, and the CGED data to fine-tune it. Being aware of the significance of precision in a grammar error correction system in real scenarios, we use ensembles to boost precision. When using inputs as simple as Chinese characters, the ensembled system achieves a precision at 86.56% in the detection of erroneous sentences, and a precision at 51.53% in the correction of errors of Selection and Missing types.

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CMMC-BDRC Solution to the NLP-TEA-2018 Chinese Grammatical Error Diagnosis Task
Yongwei Zhang | Qinan Hu | Fang Liu | Yueguo Gu
Proceedings of the 5th Workshop on Natural Language Processing Techniques for Educational Applications

Chinese grammatical error diagnosis is an important natural language processing (NLP) task, which is also an important application using artificial intelligence technology in language education. This paper introduces a system developed by the Chinese Multilingual & Multimodal Corpus and Big Data Research Center for the NLP-TEA shared task, named Chinese Grammar Error Diagnosis (CGED). This system regards diagnosing errors task as a sequence tagging problem, while takes correction task as a text classification problem. Finally, in the 12 teams, this system gets the highest F1 score in the detection task and the second highest F1 score in mean in the identification task, position task and the correction task.


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Statistical Machine Translation Improves Question Retrieval in Community Question Answering via Matrix Factorization
Guangyou Zhou | Fang Liu | Yang Liu | Shizhu He | Jun Zhao
Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

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Attribute Relation Extraction from Template-inconsistent Semi-structured Text by Leveraging Site-level Knowledge
Yang Liu | Fang Liu | Siwei Lai | Kang Liu | Guangyou Zhou | Jun Zhao
Proceedings of the Sixth International Joint Conference on Natural Language Processing


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Statistics Based Hybrid Approach to Chinese Base Phrase Identification
Tie-jun Zhao | Mu-yun Yang | Fang Liu | Jian-min Yao | Hao Yu
Second Chinese Language Processing Workshop