Christopher Bryant

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The CoNLL-2014 shared task was devoted to grammatical error correction of all error types. In this paper, we give the task definition, present the data sets, and describe the evaluation metric and scorer used in the shared task. We also give an overview of the various approaches adopted by the participating teams, and present the evaluation results.(More)
The CoNLL-2015 Shared Task is on Shallow Discourse Parsing, a task focusing on identifying individual discourse relations that are present in a natural language text. A discourse relation can be expressed explicitly or implicitly, and takes two arguments realized as sentences, clauses, or in some rare cases, phrases. Sixteen teams from three continents(More)
In this paper, we first explore the role of inter-annotator agreement statistics in grammatical error correction and conclude that they are less informative in fields where there may be more than one correct answer. We next created a dataset of 50 student essays, each corrected by 10 different annotators for all error types, and investigated how both human(More)
Until now, error type performance for Grammatical Error Correction (GEC) systems could only be measured in terms of recall because system output is not annotated. To overcome this problem, we introduce ERRANT, a grammatical ERRor ANnotation Toolkit designed to automatically extract edits from parallel original and corrected sentences and classify them(More)
The enhancer of zeste homolog 2 (EZH2) methyltransferase is a transcriptional repressor. EZH2 is abnormally elevated in epithelial ovarian cancer (EOC). We demonstrated that EZH2 knockdown inhibited cell growth, activated apoptosis, and enhanced chemosensitivity. Further, silencing of EZH2 resulted in re-expression of p21(waf1/cip1) and down-regulation of(More)
We propose a new method of automatically extracting learner errors from parallel English as a Second Language (ESL) sentences in an effort to regularise annotation formats and reduce inconsistencies. Specifically, given an original and corrected sentence, our method first uses a linguistically enhanced alignment algorithm to determine the most likely(More)
In this report, we describe some of the issues encountered when preprocessing two of the largest datasets for Grammatical Error Correction (GEC); namely the public FCE corpus and NUCLE (along with associated CoNLL test sets). In particular, we show that it is not straightforward to convert character level annotations to token level annotations and that(More)