Automated Detection of Usage Errors in non-native English Writing

In an investigation of the use of a novelty detection algorithm for identifying inappropriate word combinations in a raw English corpus, we employ an unsupervised detection algorithm based on the one- class support vector machines (OC-SVMs) and extract sentences containing word sequences whose frequency of appearance is significantly low in native English writing. Combined with n-gram language models and document categorization techniques, the OC-SVM classifier assigns given sentences into two different groups; the sentences containing errors and those without errors. Accuracies are 79.30 % with bigram model, 86.63 % with trigram model, and 34.34 % with four-gram model

GenERRate: generating errors for use in grammatical error detection

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 Learner Corpus errors using GenERRate

Correcting Errors Using the Framework of Argumentation: Towards Generating Argumentative Correction Propositions from Error Annotation Schemas

PACLIC 23 / City University of Hong Kong / 3-5 December 200

THE USE OF ARTICLES IN THE INTENSIVE COURSE STUDENTS’ COMPOSITIONS OF UNIVERSITAS NEGERI SURABAYA

Kemampuan untuk menyatakan pikiran dan pendapat baik secara lisan dan tertulis sangat penting saat ini. Hal ini diperlukan tidak hanya di pekerjaan, tetapi juga di bidang pendidikan. Oleh karena itu, siswa harus belajar dan berlatih untuk menulis dengan baik dan akurat. Keakurasian didalam penulisan itu penting untuk mendapatkan arti sebenarnya dan mengurangi kesalahan gramatikal yang dapat menyebabkan kalimat menjadi ambigu. Pembicara non-native termasuk orang Indonesia cenderung memiliki masalah yang berhubungan dengan keakurasian saat menemui hal yang tidak terlalu familiar dan berbeda dari L1 mereka. English article sangat penting karena digunakan untuk mengukur kepastian dan ketidakpastian suatu benda. Penggunaan article yang tepat dalam penulisan bahasa Inggris adalah suatu keharusan. Aturan dalam penggunaan English articles berbeda dengan penggunaan article didalam bahasa Indonesia. Studi ini merupakan penelitian deskriptif kuantitatif dalam bentuk ex post facto yang bertujuan untuk mengetahui keakurasian mahasiswa IC didalam menggunakan English article dan jenis article yang sulit untuk dikuasai oleh mahasiswa tersebut. Data yang digunakan yaitu karangan mahasiswa yang didapatkan setelah pretest dan posttest dilakukan oleh tim IC. Hasinya menunjukkan bahwa keakurasian mahasiswa dalam menggunakan English article masih rendah. Jenis article yang sulit dikuasai oleh mahasiswa IC adalah zero article Ø. Kata Kunci: Program IC, Article, Karangan Mahasiswa.   Abstract The ability to express thought and opinion both in spoken and in written is very important. It is needed not only in the job field but also in education. Therefore, students should learn and practice to write well and accurately. Accuracy in writing is important to get a good composition. It is important not only to get the actual meaning but also to reduce grammatical errors which can cause ambiguity within sentences. Non-native speakers of English including Indonesian tend to have problem in accuracy when dealing with terms which were unfamiliar and different from their L1. Articles are very important in English due to its necessity in measuring definiteness and indefiniteness of nouns. It is a must in English to use the correct article. The rules in using English articles are different from using articles in Bahasa Indonesia. This study was a descriptive quantitative in the form of ex post facto research which was aimed to find out the accuracy of the IC students in using the English articles and the most difficult article for them. The data are the students’ compositions obtained after a pretest and a posttest were administered by the IC team. The result shows that the students’ accuracy on the use of articles is still low. The most difficult article for the IC students was zero article Ø. Keywords: IC Program, Articles, Composition.   &nbsp

HOO 2012 Error Recognition and Correction Shared Task: Cambridge University Submission Report

Previous work on automated error recognition and correction of texts written by learners of English as a Second Language has demonstrated experimentally that training classifiers on error-annotated ESL text generally outperforms training on native text alone and that adaptation of error correction models to the native language (L1) of the writer improves performance. Nevertheless, most extant models have poor precision, particularly when attempting error correction, and this limits their usefulness in practical applications requiring feedback. We experiment with various feature types, varying quantities of error-corrected data, and generic versus L1-specific adaptation to typical errors using Naïve Bayes (NB) classifiers and develop one model which maximizes precision. We report and discuss the results for 8 models, 5 trained on the HOO data and 3 (partly) on the full error-coded Cambridge Learner Corpus, from which the HOO data is drawn.We thank Cambridge ESOL, a division of Cambridge Assessment for a partial grant to the first author and a research contract with iLexIR Ltd. We also thank them and Cambridge University Press for granting us access to the CLC for research purposes

Judging grammaticality: experiments in sentence classification

A classifier which is capable of distinguishing a syntactically well formed sentence from a syntactically ill formed one has the potential to be useful in an L2 language-learning context. In this article, we describe a classifier which classifies English sentences as either well formed or ill formed using information gleaned from three different natural language processing techniques. We describe the issues involved in acquiring data to train such a classifier and present experimental results for this classifier on a variety of ill formed sentences. We demonstrate that (a) the combination of information from a variety of linguistic sources is helpful, (b) the trade-off between accuracy on well formed sentences and accuracy on ill formed sentences can be fine tuned by training multiple classifiers in a voting scheme, and (c) the performance of the classifier is varied, with better performance on transcribed spoken sentences produced by less advanced language learners

A lexicographic approach to teaching the English article system: help or hindrance?

This article reports on changes in EFL learners' article choice performance before and after receiving lessons on the main rules applicable to article usage combined with dictionary con-sultation guidance. A sample of 43 Korean college students undertook the same forced-choice elicitation task once as a diagnostic test and again as a post-intervention test at three-month inter-vals. Unlike the diagnostic test, in which the participants were only asked to choose the correct articles, the post-intervention test asked them to give written accounts of their decision-making procedures as well. The analyses of the diagnostic test results, specifically the items requiring the indefinite article or the zero article, demonstrated EFL learners' struggle with indeterminate nomi-nal numbers, underlining the importance of clear lexicographic treatment of such information. Further, the post-intervention test and the written think-aloud data analyses suggested that although using a bilingualised dictionary for nominal countability is useful in general, dictionary consultation can sometimes impede users from using articles correctly. Specific problem areas are discussed.Keywords: English article system, nominal countability, article use, bilingualised dictionary, Korean EFL learner