[PDF] Top 20 Native Language Identification With Classifier Stacking and Ensembles

... of classifier ensembles for NLI, and they performed a comprehensive evaluation of the feature types used until that ...using language models for this task and in their system used language ... See full document

... Native language identification (NLI) is the task of determining an author’s native language (L1) based on their writings in a second language ...particular language ... See full document

... Native Language Identification is most commonly tackled as a multi-class supervised classification task combining NLP–enabled feature extraction and machine learning: see ...of classifier ... See full document

... Building on the idea of ensembles and meta- classification, we experiment with ensembles of meta-classifiers (Malmasi and Dras, 2017). SVM and FCNN outputs—meta-features—are gener- ated in the same way as ... See full document

... non- native English speakers of eleven native languages taking a standardized assessment of English profi- ciency for academic ...purposes. Native languages included are: Arabic, Chinese, French, ... See full document

... Prior work has shown that ensemble classification can improve NLI performance. Tetreault et al. (2012) established that ensembles composed of classifiers trained on different feature types were useful for NLI and ... See full document

... to classifier ensembles, meta-classifier systems have proven to be very competitive for text classification tasks (Malmasi and Zampieri, 2016) and we decided to include a meta-classifier in ... See full document

... most ensembles gain over the early fusion approach, but the improvement is limited to less than ...following ensembles (“Best CV”, “Top15” and “Best ... See full document

... created language model perplexity scores that reflected the lexical 5-grams most representative of each L1 in the ...separate classifier models for each category of features; the L1 affiliations of ... See full document

... Despite the fact that the approach based on string kernels performed so well, it remains to be further investigated why this is the case and why such a simple approach can compete with far more complex approaches that ... See full document

... Rangel and Rosso (2013; 2016) investigate and confirm the hypothesis that the use of emotions depends on author’s age and gender. The au- thors used a graph-based approach, where each node and edge were represented by ... See full document

... each language with a single mix- ture Gaussian distribution with full covariance ma- trix shared across different target languages since it has proven very effective (Martınez et ...baseline classifier: a ... See full document

... using ensembles or meta- classifiers with a large variety of word and character n-gram features that yield top performances in ADI (Malmasi and Zampieri, 2017a) (second place) and GDI (Malmasi and Zampieri, 2017b) ... See full document

... We experiment using three syntactic feature types described in this section. As the ASK corpus is not balanced for topic, we do not consider the use of lexical features such as word n-grams in this study. Topic bias can ... See full document

... Another related issue is whether sub-lexical char- acter n-grams are independent of word features. Previously, Tsur and Rappoport (2007) hypothe- sized that these n-grams are discriminative due to writer choices ... See full document

... learning classifier with the following types of features: (i) word, lemma and character n-grams, (ii) function words (FW), (iii) part-of-speech (POS) n-grams, (iv) adaptor grammar collocations, (v) Stanford ... See full document

... The character-based PPM models were used for spam detection, source-based text classification and classification of multi-modal data streams that included texts. In Bratko et al. (2006), the charac- ter-based PPM models ... See full document

... We experiment using three syntactic feature types described in this section. As the ALC is not bal- anced for topic, we do not consider the use of lex- ical features such as word n-grams in this study. Topic bias can ... See full document

... In this paper we describe the language identification system we developed for the Discriminating Similar Languages (DSL) 2015 shared task. We constructed a clas- sifier ensemble composed of several Sup- ... See full document

... This challenge motivated the organization of a number of competitions such as the Discriminating between Similar Languages (DSL) shared tasks which included language varieties and similar languages (Zampieri et ... See full document