YNU-HPCC at IJCNLP-2017 Task 1: Chinese Grammatical Error Diagnosis Using a Bi-directional LSTM-CRF Model
نویسندگان
چکیده
Building a system to detect Chinese grammatical errors is a challenge for naturallanguage processing researchers. As Chinese learners are increasing, developing such a system can help them study Chinese more easily. This paper introduces a bidirectional long short-term memory (BiLSTM) conditional random field (CRF) model to produce the sequences that indicate an error type for every position of a sentence, since we regard Chinese grammatical error diagnosis (CGED) as a sequence-labeling problem. Among the participants this year of CGED shard task, our model ranked third in the detectionlevel and identification-level results. In the position-level, our results ranked second among the participants.
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