Universal conceptual cognitive annotation (UCCA). Finally, we discuss five prospective directions for future GEC researches. More importantly, after the introduction of the evaluation in GEC, we make an in-depth analysis based on empirical results in aspects of GEC approaches and GEC systems for a clearer pattern of progress in GEC, where error type analysis and system recapitulation are clearly presented. Similarly, some performance-boosting techniques are adapted from MT and are successfully combined with GEC systems for enhancement on the final performance. Since GEC is typically viewed as a sister task of Machine Translation (MT), we put more emphasis on the statistical machine translation (SMT)-based approaches and neural machine translation (NMT)-based approaches for the sake of their importance. After that, we discuss six kinds of basic approaches, six commonly applied performance boosting techniques for GEC systems, and three data augmentation methods. We first give the definition of GEC task and introduce the public datasets and data annotation schema. We present the first survey in GEC for a comprehensive retrospective of the literature in this area. However, there is not a survey that untangles the large amount of research works and progress in this field. The past decade has witnessed significant progress achieved in GEC for the sake of increasing popularity of machine learning and deep learning. Grammatical error correction (GEC) is an important application aspect of natural language processing techniques, and GEC system is a kind of very important intelligent system that has long been explored both in academic and industrial communities.
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