Condition Random Fields-based Grammatical Error Detection for Chinese as Second Language

Abstract

The foreign learners are not easy to learn Chinese as a second language. Because there are many special rules different from other languages in Chinese. When the people learn Chinese as a foreign language usually make some grammatical errors, such as missing, redundant, selection and disorder. In this paper, we proposed the conditional random fields (CRFs) to detect the grammatical errors. The features based on statistical word and part-ofspeech (POS) pattern were adopted here. The relationships between words by part-of-speech are helpful for Chinese grammatical error detection. Finally, we according to CRF determined which error types in sentences. According to the observation of experimental results, the performance of the proposed model is acceptable in precision and recall rates.

An Algorithm for Automatic Recognition of Chinese Spatial Prepositional Phrases for Academic Texts

“…The problem of preposition disambiguation is another issue that is closely related to this one. These two issues are syntactic ambiguity and semantic ambiguity, both of which are inextricably linked in their nature [6][7][8][9][10].…”

Developing learner corpus annotation for Chinese grammatical errors

“…The NTOU system proposed two sentence likelihood functions based on frequencies of Google n-grams to diagnose grammatical errors (Lin, & Chen, 2015). The NCYU system also used statistical word and part-of-speech patterns based CRFs to detect grammatical errors (Yeh et al, 2015). The TMU examined corpus augmentation and explored syntax-based and hierarchical phrase-based translation models for use in this task (Zhao et al 2015).…”