21,673 research outputs found
A comparative evaluation of deep and shallow approaches to the automatic detection of common grammatical errors
This paper compares a deep and a shallow processing approach to the problem of classifying a sentence as grammatically wellformed or ill-formed. The deep processing
approach uses the XLE LFG parser and English grammar: two versions are presented, one which uses the XLE directly to perform the classification, and another one which uses a decision tree trained on features consisting of the XLE’s output statistics. The shallow processing approach predicts grammaticality based on n-gram frequency statistics:
we present two versions, one which uses frequency thresholds and one which uses a decision tree trained on the frequencies of the rarest n-grams in the input sentence.
We find that the use of a decision tree improves on the basic approach only for the deep parser-based approach. We also show that combining both the shallow and deep
decision tree features is effective. Our evaluation
is carried out using a large test set of grammatical and ungrammatical sentences. The ungrammatical test set is generated automatically by inserting grammatical errors
into well-formed BNC sentences
JFLEG: A Fluency Corpus and Benchmark for Grammatical Error Correction
We present a new parallel corpus, JHU FLuency-Extended GUG corpus (JFLEG) for
developing and evaluating grammatical error correction (GEC). Unlike other
corpora, it represents a broad range of language proficiency levels and uses
holistic fluency edits to not only correct grammatical errors but also make the
original text more native sounding. We describe the types of corrections made
and benchmark four leading GEC systems on this corpus, identifying specific
areas in which they do well and how they can improve. JFLEG fulfills the need
for a new gold standard to properly assess the current state of GEC.Comment: To appear in EACL 2017 (short papers
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