Misuse of Chinese prepositions is one of common word usage errors in grammatical error diagnosis. In this paper, we adopt the Chinese Gigaword corpus and HSK corpus as L1 and L2 corpora, respectively. We explore gated recurrent neural network model (GRU), and an ensemble of GRU model and maximum entropy language model (GRU-ME) to select the best preposition from 43 candidates for each test sent...

We investigate the application of weighted tree transducers to correcting grammatical errors in natural language. Weighted finite-state transducers (FST) have been used successfully in a wide range of natural language processing (NLP) tasks, even though the expressiveness of the linguistic transformations they perform is limited. Recently, there has been an increase in the use of weighted tree ...

The present study-both qualitative and quantitative--explored fifty EFL learners’ preferences for receiving error feedback on different grammatical units as well as their beliefs about teacher feedback strategies. The study also examined the effect of the students’ level of writing ability on their views about the importance of teacher feedback on different error types. Data was gathered throug...

Abstract - The objective of this study is to know the most dominant grammatical error made by seventh-grade students SMP Trisula Perwari 2 in writing recount text and reason for errors. writer used Betty Azar’s types theory Richards’ sources theory. method was a descriptive analysis describe students’ errors analyze data. research findings show that add word holds first position with 154 or 20%...

We present a novel approach to grammatical error correction based on Alternating Structure Optimization. As part of our work, we introduce the NUS Corpus of Learner English (NUCLE), a fully annotated one million words corpus of learner English available for research purposes. We conduct an extensive evaluation for article and preposition errors using various feature sets. Our experiments show t...

This paper presents a hybrid model for the CoNLL-2013 shared task which focuses on the problem of grammatical error correction. This year’s task includes determiner, preposition, noun number, verb form, and subject-verb agreement errors which is more comprehensive than previous error correction tasks. We correct these five types of errors in different modules where either machine learning based...

The paper presents the results of the first large-scale human evaluation of automatic grammatical error correction (GEC) systems. Twelve participating systems and the unchanged input of the CoNLL-2014 shared task have been reassessed in a WMT-inspired human evaluation procedure. Methods introduced for the Workshop of Machine Translation evaluation campaigns have been adapted to GEC and extended...

We present a novel method for evaluating grammatical error correction. The core of our method, which we call MaxMatch (M), is an algorithm for efficiently computing the sequence of phrase-level edits between a source sentence and a system hypothesis that achieves the highest overlap with the goldstandard annotation. This optimal edit sequence is subsequently scored using F1 measure. We test our...