You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
This paper presents the first attempt, up to our knowledge, to classify English writing styles on this scale with the challenge of classifying day to day language written by writers with different backgrounds covering various areas of topics.The paper proposes simple machine learning algorithms and simple to generate features to solve hard problems. Relying on the scale of the data available from large sources of knowledge like Wikipedia. We believe such sources of data are crucial to generate robust solutions for the web with high accuracy and easy to deploy in practice. The paper achieves 74 accuracy classifying native versus non native speakers writing styles. Moreover, the paper shows some interesting observations on the similarity between different languages measured by the similarity of their users English writing styles. This technique could be used to show some well known facts about languages as in grouping them into families, which our experiments support.

Cited Abstract(s):
[1]: Text authored by an unidentified assailant can offer valuable clues to the assailant's identity. In this paper, we show that stylistic text features can be exploited to determine an anonymous author's native language with high accuracy.
The first work related with native language identification is that of [1] , in which they tried profiling anonymous authors with their native languages. Totally five different groups of English authors (whose native languages are Russian, Bulgarian, French, and Spanish) were picked from the first version of International Corpus of Learner English (ICLE) in their experiments. By applying a combined feature sets, including function words, character n-grams, part-of-speech bi-grams and spelling mistakes, they gained an accuracy of 65 Similar work was done by tsur2007using . They focused on the relationships between choice of words in second language writing and the frequency of native language syllables, also known as the phonology of native languages. estival2007author studied a wide range of lexical and document structure features in their native languages classification task. And zheng2003authorship , though they did not directly conduct related experiments on nationality detection, they provide some features of style markes that could be used in the task of judging one's native languages. Besides, gamon2004linguistic analysized the power of some general features under different frequency cutoffs. But none of these measured the usefulness of syntactic features under a general condition for the task of native language detection.