We propose a joint inference algorithm for grammatical error correction. Different from most previous work where different error types are corrected independently, our proposed inference process considers all possible errors in a uni ed framework. We use integer linear programming (ILP) to model the inference process, which can easily incorporate both the power of existing error classi ers and ...

This paper introduces Alibaba NLP team system on IJCNLP 2017 shared task No. 1 Chinese Grammatical Error Diagnosis (CGED). The task is to diagnose four types of grammatical errors which are redundant words (R), missing words (M), bad word selection (S) and disordered words (W). We treat the task as a sequence tagging problem and design some handcraft features to solve it. Our system is mainly b...

We investigate grammatical error detection in spoken language, and present a data-driven method to train a dependency parser to automatically identify and label grammatical errors. This method is agnostic to the label set used, and the only manual annotations needed for training are grammatical error labels. We find that the proposed system is robust to disfluencies, so that a separate stage to...

Previous words in the sentence can influence the processing of the current word in the timescale of hundreds of milliseconds. The current research provides a possible explanation of how certain aspects of this on-line language processing can occur, based on the dynamics of recurrent cortical networks. We simulate prefrontal area BA47 as a recurrent network that receives on-line input of “gramma...

This article provides description about the grammar checking software developed for detecting the grammatical errors in Punjabi texts and providing suggestions wherever appropriate to rectify those errors. This system utilizes a full-form lexicon for morphology analysis and rule-based systems for part of speech tagging and phrase chunking. The system supported by a set of carefully devised erro...

Selecting the optimal topology of a neural network for a particular application is a dicult task. In the case of recurrent neural networks, most methods only induce topologies in which their neurons are fully connected. In this paper, we present a genetic algorithm capable of obtaining not only the optimal topology of a recurrent neural network but also the least number of connections necessar...

This article describes how a treebank of ungrammatical sentences can be created from a treebank of well-formed sentences. The treebank creation procedure involves the automatic introduction of frequently occurring grammatical errors into the sentences in an existing treebank, and the minimal transformation of the original analyses in the treebank so that they describe the newly created ill-form...

Our work on author identification and author profiling is based on the question: Can the number and the types of grammatical errors serve as indicators for a specific author or a group of people? In order to detect the grammatical errors we base our approach on the output of the open-source library LanguageTool. In the case of the author identification we transform the problem into a statistica...