Papers Native Language Identification

CIC-FBK Approach to Native Language Identification

We present the CIC-FBK system, which took part in the Native Language Identification (NLI) Shared Task 2017. Our approach combines features commonly used in previous NLI research, i.e., word n-grams, lemma n-grams, part-…

The Power of Character N-grams in Native Language Identification

In this paper, we explore the performance of a linear SVM trained on language independent character features for the NLI Shared Task 2017. Our basic system (GRONINGEN) achieves the best performance (87.56 F1-score) on th…

Classifier Stacking for Native Language Identification

This paper reports our contribution (team WLZ) to the NLI Shared Task 2017 (essay track). We first extract lexical and syntactic features from the essays, perform feature weighting and selection, and train linear support…

Native Language Identification using Phonetic Algorithms

In this paper, we discuss the results of the IUCL system in the NLI Shared Task 2017. For our system, we explore a variety of phonetic algorithms to generate features for Native Language Identification. These features ar…

A deep-learning based native-language classification by using a latent semantic analysis for the NLI Shared Task 2017

This paper proposes a deep-learning based native-language identification (NLI) using a latent semantic analysis (LSA) as a participant (ETRI-SLP) of the NLI Shared Task 2017 where the NLI Shared Task 2017 aims to detect …

Fusion of Simple Models for Native Language Identification

In this paper we describe the approaches we explored for the 2017 Native Language Identification shared task. We focused on simple word and sub-word units avoiding heavy use of hand-crafted features. Following recent tre…

Stacked Sentence-Document Classifier Approach for Improving Native Language Identification

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 fi…

Vector Space Model as Cognitive Space for Text Classification

In this era of digitization, knowing the user's sociolect aspects have become essential features to build the user specific recommendation systems. These sociolect aspects could be found by mining the user's language sha…

Can string kernels pass the test of time in Native Language Identification?

We describe a machine learning approach for the 2017 shared task on Native Language Identification (NLI). The proposed approach combines several kernels using multiple kernel learning. While most of our kernels are based…

Native Language Identification on Text and Speech

This paper presents an ensemble system combining the output of multiple SVM classifiers to native language identification (NLI). The system was submitted to the NLI Shared Task 2017 fusion track which featured students e…

Improving Native Language Identification by Using Spelling Errors

In this paper, we explore spelling errors as a source of information for detecting the native language of a writer, a previously under-explored area. We note that character n-grams from misspelled words are very indicati…

Learning with learner corpora: Using the TLE for native language identification

Native Language Identification using Stacked Generalization

Ensemble methods using multiple classifiers have proven to be the most successful approach for the task of Native Language Identification (NLI), achieving the current state of the art. However, a systematic examination o…

Advancing Linguistic Features and Insights by Label-informed Feature Grouping: An Exploration in the Context of Native Language Identification

We propose a hierarchical clustering approach designed to group linguistic features for supervised machine learning that is inspired by variationist linguistics. The method makes it possible to abstract away from the ind…

String Kernels for Native Language Identification: Insights from Behind the Curtains

Feature Extraction for Native Language Identification Using Language Modeling

Norwegian Native Language Identification

Measuring Feature Diversity in Native Language Identification

Oracle and Human Baselines for Native Language Identification

Large-Scale Native Language Identification with Cross-Corpus Evaluation