Native Language Identification (NLI) is a task aimed at determining the native language (L1) of learners of second language (L2) on the basis of their written texts. To date, research on NLI has focused on relatively small corpora. We apply NLI to the recently released EFCamDat corpus which is not only multiple times larger than previous L2 corpora but also provides longitudinal data at several...

This paper aims at investigating the acquisition of Italian complex predicates by native speakers of Persian. Complex predication is not as pervasive a phenomenon in Italian as it is in Persian. Yet Italian native speakers use complex predicates productively; spontaneous data show that Persian learners of Italian seem to be perfectly aware of Italian complex predicates and use this familiar fea...

In this paper, we describe the approach of the ItaliaNLP Lab team to native language identification and discuss the results we submitted as participants to the essay track of NLI Shared Task 2017. We introduce for the first time a 2-stacked sentencedocument architecture for native language identification that is able to exploit both local sentence information and a wide set of general–purpose f...

We present a system for automatically identifying the native language of a writer. We experiment with a large set of features and train them on a corpus of 9,900 essays written in English by speakers of 11 different languages. our system achieved an accuracy of 43% on the test data, improved to 63% with improved feature normalization. In this paper, we present the features used in our system, d...

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...