[PDF] Top 20 Neural Networks and Spelling Features for Native Language Identification

... To reduce overfitting, we perform training by choosing a random subset of 50% of the sentences in an essay, concatenating their PoS tag and token embeddings, and running the resulting vector se- quence through a ... See full document

... lexical features for Native Language Identification (NLI) within the framework of ensemble ...performing features, we train Support Vector Machines (SVM) and Fully Con- nected ... See full document

... the features in these two categories. Word-level spelling errors when used on their own do not perform well, with one of the causes being ...other features (word n-grams and lemma n-grams), word ... See full document

... lexical features, Kochmar presented some results using word n-grams, but regarded them as attributable to topic bias in the ...Error-type features (e.g. spelling, missing determiner) as provided by ... See full document

... lexical features such as number of characters, number of syllables, num- ber of synonyms, word n-gram, POS tags, depen- dency parse relations, number of words grammat- ically related to the target word, and Google ... See full document

... We then use the regular LM training recipe in the RNNLM toolkit that makes use of a validation data set. We adjust the values of various hyper- parameters in our experiments. One crucial pa- rameter we adjust is the ... See full document

... informative features for French, for example, we find developp from the French d´evelopp´e which leads to a misspelling of the En- glish word ...of features that indicate ...making spelling English ... See full document

... each native language have their own learning experiences which are re- flected in their ...Audio features in the form of i-vectors help to improve the perfor- ... See full document

... Word networks can be constructed either by respecting sentence boundaries (where the last word of sentence 1 does not link to the first word of sentence 2), or by disregard- ing ... See full document

... The features in their model included character n-grams, function words, parts of speech, spelling errors and features of writing quality, such as grammatical errors, style markers, and so ...created ... See full document

... of features that included character and word n-grams, function words, parts of speech, spelling errors, and writing quality ...syntactic features, they also investigated the use of Tree Substitution ... See full document

... best-performing features: char- acter, word and part-of-speech n-grams along with features inspired by the work in the area of second- language acquisition such as spelling and gram- matical ... See full document

... for Native Language Identification (NLI), a task that has received much attention in recent ...the native language (L1) of an author based on their second language (L2) ... See full document

... The second model (combined positioned) was trained with a word-order sensitive representation of the aforementioned features. Both the TF- IDF vector and the sentiment vectors were con- structed with length |word ... See full document

... 11 native languages of the test takers are: Arabic (ARA), Chinese (CHI), French (FRE), German (GER), Hindi (HIN), Italian (ITA), Japanese (JPN), Ko- rean (KOR), Spanish (SPA), Telugu (TEL), and Turkish ... See full document

... Native Language Identification can be used in educational ...to language learners about their ...the native language can be used as a feature for authorship analysis (Sta- ... See full document

... of features and classifiers, what is the maximal performance that could be achieved by an NLI system that always picks the best candidate? This question, not previously addressed in the context of NLI to date, is ... See full document

... the native language of an author based on texts written in a second language has generally been tackled as a clas- sification problem, typically using as features a mix of n-grams over ... See full document

... of features and guide interpre- tation of the ...the identification of the most diverse independent learn- ers and this method can be applied to that ... See full document

... emoP features, significantly contribute to the results for all the considered set- tings, indicating that different cultures (as defined by the authors’ L1) have different emotion word ...emoL features – 13 ... See full document