We present a new treebank of English and French technical forum content which has been annotated for grammatical errors and phrase structure. This double annotation allows us to empirically measure the effect of errors on parsing performance. While it is slightly easier to parse the corrected versions of the forum sentences, the errors are not the main factor in making this kind of text hard to...

Many people are learning English as a second or foreign language, and there are estimated 375 million English as a Second Language (ESL) and 750 million English as a Foreign Language (EFL) learners around the world according to Graddol (2006). Evidently, automatic grammar checkers are much needed to help learners improve their writing. However, typical English proofreading tools do not target s...

We introduce here a participating system of the CoNLL-2013 Shared Task “Grammatical Error Correction”. We focused on the noun number and article error categories and constructed a supervised learning system for solving these tasks. We carried out feature engineering and we found that (among others) the f-structure of an LFG parser can provide very informative features for the machine learning s...

In this paper, we propose a new model for refining SMS text messages where two different kinds of grammatical errors frequently occur together. A two-phase approach based on the divide and conquer strategy is presented where HMM-based model is used for correcting spacing errors in the first phase, and rule-based correction model is used for correcting spelling errors in the second phase. Experi...

To speed up the process of categorizing learner errors and obtaining data for languages which lack error-annotated data, we describe a linguistically-informed method for generating learner-like morphological errors, focusing on Russian. We outline a procedure to select likely errors, relying on guiding stem and suffix combinations from a segmented lexicon to match particular error categories an...

We parse the sentences in three parallel error corpora using a generative, probabilistic parser and compare the parse probabilities of the most likely analyses for each grammatical sentence and its closely related ungrammatical counterpart.

Spontaneous writing samples of deaf children with cochlear implants were analyzed for syntactic errors and other descriptive characteristics. These results were compared to a small sample of writings from hearing children.

Learning a foreign language requires much practice outside of the classroom. Computerassisted language learning systems can help fill this need, and one desirable capability of such systems is the automatic correction of grammatical errors in texts written by non-native speakers. This dissertation concerns the correction of non-native grammatical errors in English text, and the closely related ...

This paper explores the issue of automatically generated ungrammatical data and its use in error detection, with a focus on the task of classifying a sentence as grammatical or ungrammatical. We present an error generation tool called GenERRate and show how GenERRate can be used to improve the performance of a classifier on learner data. We describe initial attempts to replicate Cambridge Learn...