Native Language Identification (NLI), which tries to identify the native language (L1) of a second language learner based on their writings, is helpful for advancing second language learning and authorship profiling in forensic linguistics. With the availability of relevant data resources, much work has been done to explore the native language of a foreign language learner. In this report, we p...

The purpose of this study is to describe and analyse the interference Kazakh Russian languages at lexical level as a result interaction two identify special features use language among Kazakhs. material results an experiment conducted in school with 29 pupils Grade 9 instruction Nur-Sultan Lyceum School No. 48. first part consists fact that subjects were offered words which they had form phrase...

Previous approaches to the task of native language identification (Koppel et al., 2005) have been limited to small, within-corpus evaluations. Because these are restrictive and unreliable, we apply cross-corpus evaluation to the task. We demonstrate the efficacy of lexical features, which had previously been avoided due to the within-corpus topic confounds, and provide a detailed evaluation of ...

The MERLIN corpus is a written learner corpus for Czech, German, and Italian that has been designed to illustrate the Common European Framework of Reference for Languages (CEFR) with authentic learner data. The corpus contains 2,290 learner texts produced in standardized language certifications covering CEFR levels A1–C1. The MERLIN annotation scheme includes a wide range of language characteri...

The task of Native Language Identification (NLI) is typically solved with machine learning methods, and systems make use of a wide variety of features. Some preliminary studies have been conducted to examine the effectiveness of individual features, however, no systematic study of feature interaction has been carried out. We propose a function to measure feature independence and analyze its eff...

This paper describes the Nara Institute of Science and Technology (NAIST) native language identification (NLI) system in the NLI 2013 Shared Task. We apply feature selection using a measure based on frequency for the closed track and try Capping and Sampling data methods for the open tracks. Our system ranked ninth in the closed track, third in open track 1 and fourth in open track 2.

We decribe the submissions made by the National Research Council Canada to the Native Language Identification (NLI) shared task. Our submissions rely on a Support Vector Machine classifier, various feature spaces using a variety of lexical, spelling, and syntactic features, and on a simple model combination strategy relying on a majority vote between classifiers. Somewhat surprisingly, a classi...

Native Language Identification, or NLI, is the task of automatically classifying the L1 of a writer based solely on his or her essay written in another language. This problem area has seen a spike in interest in recent years as it can have an impact on educational applications tailored towards non-native speakers of a language, as well as authorship profiling. While there has been a growing bod...