A Report on the First Native Language Identification Shared Task
Joel Tetreault,Daniel Blanchard,Aoife Cahill +2 more
- pp 48-57
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The fusion track showed that combining the written and spoken responses provides a large boost in prediction accuracy, and multiple classifier systems were the most effective in all tasks, with most based on traditional classifiers with lexical/syntactic features.Citations
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Language (Technology) is Power: A Critical Survey of "Bias" in NLP
TL;DR: The authors survey 146 papers analyzing "bias" in NLP systems, finding that their motivations are often vague, inconsistent, and lacking in normative reasoning, despite the fact that analyzing bias is an inherently normative process.
Journal Article
Learner English: A Teacher's Guide to Interference and Other Problems Second Edition [Book Review]
TL;DR: Review(s) of: Learner English: A Teacher's Guide to Interference and Other Problems, Second Edition, by Michael Swan and Bernard Smith.
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Syntactic complexity in college-level English writing: Differences among writers with diverse L1 backgrounds
Xiaofei Lu,Haiyang Ai +1 more
TL;DR: Differences in the syntactic complexity in English writing among college-level writers with different first language (L1) backgrounds are explored and varied patterns for L2 writing research and pedagogy and for automatic native language identification of learner texts are considered.
Proceedings ArticleDOI
Do Characters Abuse More Than Words
Yashar Mehdad,Joel Tetreault +1 more
TL;DR: This study investigates the effectiveness of character-based features for abusive language detection in user-generated online comments, and shows that such methods outperform previous state-of-theart approaches and other strong baselines.
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Toefl11: a corpus of non‐native english
TL;DR: A new corpus of non-native English writing will be useful for the task of native language identification, as well as grammatical error detection and correction, and automatic essay scoring.
References
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Proceedings ArticleDOI
A re-examination of text categorization methods
Yiming Yang,Xin Liu +1 more
TL;DR: The results show that SVM, kNN and LLSF signi cantly outperform NNet and NB when the number of positive training instances per category are small, and that all the methods perform comparably when the categories are over 300 instances.
Decision templates for multiple classi"er fusion: an experimental comparison
TL;DR: This work presents here a simple rule for adapting the class combiner to the application and shows that decision templates based on integral type measures of similarity are superior to the other schemes on both data sets.
Journal ArticleDOI
Decision templates for multiple classifier fusion: an experimental comparison.
TL;DR: In this article, a simple rule for adapting the class combiner to the application is presented, where decision templates (one per class) are estimated with the same training set that is used for the set of classifiers.
Journal ArticleDOI
Comparison of four approaches to automatic language identification of telephone speech
TL;DR: Four approaches for automatic language identification of speech utterances are compared: Gaussian mixture model (GMM) classification; single-language phone recognition followed by languaged dependent, interpolated n-gram language modeling (PRLM); parallel PRLM, which uses multiple single- language phone recognizers, each trained in a different language; and languagedependent parallel phone recognition (PPR).
Proceedings Article
A New Dataset and Method for Automatically Grading ESOL Texts
TL;DR: It is demonstrated how supervised discriminative machine learning techniques can be used to automate the assessment of 'English as a Second or Other Language' (ESOL) examination scripts by using rank preference learning to explicitly model the grade relationships between scripts.