A fundamental question in language learning concerns the role of a speaker's first language in second language acquisition. We present a novel methodology for studying this question: analysis of eye-movement patterns in second language reading of free-form text. Using this methodology, we demonstrate for the first time that the native language of English learners can be predicted from their gaze fixations when reading English. We provide analysis of classifier uncertainty and learned features, which indicates that differences in English reading are likely to be rooted in linguistic divergences across native languages. The presented framework complements production studies and offers new ground for advancing research on multilingualism.
 Linguists and psychologists have long been studying cross-linguistic transfer, the influence of native language properties on linguistic performance in a foreign language. In this work we provide empirical evidence for this process in the form of a strong correlation between language similarities derived from structural features in English as Second Language (ESL) texts and equivalent similarities obtained from the typological features of the native languages. We leverage this finding to recover native language typological similarity structure directly from ESL text, and perform prediction of typological features in an unsupervised fashion with respect to the target languages. Our method achieves 72.2 accuracy on the typology prediction task, a result that is highly competitive with equivalent methods that rely on typological resources. In this paper, we show that stylistic text features can be exploited to determine an anonymous author's native language with high accuracy. Specifically, we first use automatic tools to ascertain frequencies of various stylistic idiosyncrasies in a text. These frequencies then serve as features for support vector machines that learn to classify texts according to author native language.   We apply machine learning techniques to study language transfer, a major topic in the theory of Second Language Acquisition (SLA). Using an SVM for the problem of native language classification, we show that a careful analysis of the effects of various features can lead to scientific insights. In particular, we demonstrate that character bigrams alone allow classification levels of about 66 for a 5-class task, even when content and function word differences are accounted for. This may show that native language has a strong effect on the word choice of people writing in a second language. This work examines the impact of crosslinguistic transfer on grammatical errors in English as Second Language (ESL) texts. Using a computational framework that formalizes the theory of Contrastive Analysis (CA), we demonstrate that language specific error distributions in ESL writing can be predicted from the typological properties of the native language and their relation to the typology of English. Our typology driven model enables to obtain accurate estimates of such distributions without access to any ESL data for the target languages. Furthermore, we present a strategy for adjusting our method to low-resource languages that lack typological documentation using a bootstrapping approach which approximates native language typology from ESL texts. Finally, we show that our framework is instrumental for linguistic inquiry seeking to identify first language factors that contribute to a wide range of difficulties in second language acquisition. Mother tongue interference is the phenomenon where linguistic systems of a mother tongue are transferred to another language. Recently, Nagata and Whittaker (2013) have shown that language family relationship among mother tongues is preserved in English written by IndoEuropean language speakers because of mother tongue interference. At the same time, their findings further introduce the following two research questions: (1) Does the preservation universally hold in non-native English other than in English of Indo-European language speakers? (2) Is the preservation independent of proficiency in English? In this paper, we address these research questions. We first explore the two research questions empirically by reconstructing language family trees from English texts written by speakers of Asian languages. We then discuss theoretical reasons for the empirical results. We finally introduce another hypothesis called the existence of a probabilistic module to explain why the preservation does or does not hold in particular situations.  Language transfer, the preferential second language behavior caused by similarities to the speaker’s native language, requires considerable expertise to be detected by humans alone. Our goal in this work is to replace expert intervention by data-driven methods wherever possible. We define a computational methodology that produces a concise list of lexicalized syntactic patterns that are controlled for redundancy and ranked by relevancy to language transfer. We demonstrate the ability of our methodology to detect hundreds of such candidate patterns from currently available data sources, and validate the quality of the proposed patterns through classification experiments. Language transfer, the characteristic second language usage patterns caused by native language interference, is investigated by Second Language Acquisition (SLA) researchers seeking to find overused and underused linguistic features. In this paper we develop and present a methodology for deriving ranked lists of such features. Using very large learner data, we show our method’s ability to find relevant candidates using sophisticated linguistic features. To illustrate its applicability to SLA research, we formulate plausible language transfer hypotheses supported by current evidence. This is the first work to extend Native Language Identification to a broader linguistic interpretation of learner data and address the automatic extraction of underused features on a per-native language basis. We develop a method for effective extraction of linguistic patterns that are differentially expressed based on the native language of the author. This method uses multiple corpora to allow for the removal of data set specific patterns, and addresses both feature relevancy and redundancy. We evaluate different relevancy ranking metrics and show that common measures of relevancy can be inappropriate for data with many rare features. Our feature set is a broad class of syntactic patterns, and to better capture the signal we extend the Bayesian Tree Substitution Grammar induction algorithm to a supervised mixture of latent grammars. We show that this extension can be used to extract a larger set of relevant features. In this paper we present work on the task of Native Language Identification (NLI). We present an alternative corpus to the ICLE which has been used in most work up until now. We believe that our corpus, TOEFL11, is more suitable for the task of NLI and will allow researchers to better compare systems and results. We show that many of the features that have been commonly used in this task generalize to new and larger corpora. In addition, we examine possible ways of increasing current system performance (e.g., additional features and feature combination methods), and achieve overall state-of-the-art results (accuracy of 90.1 ) on the ICLE corpus using an ensemble classifier that includes previously examined features and a novel feature (n-gram language models). We also show that training on a large corpus and testing on a smaller one works well, but not vice versa. Finally, we show that system performance varies across proficiency scores.