[PDF] Top 20 Feature Space Selection and Combination for Native Language Identification

... Our results suggest that on the shared task, a combi- nation of features relying only on word and character ngrams provided a strong baseline. Our best system ended up being a combination of models trained on ... See full document

... of feature could be in the context of ...a combination of lexical and syntactic features, including short character and word ngrams, part-of-speech and syntactic depen- ... See full document

... We applied the system described in the previous section to the TOEFL11 corpus. We did this in multiple stages, first by training the system on the original training set of 9,900 texts while using LIBLINEAR’s built-in ... See full document

... the feature representations and see a clear preference for frequency-based fea- ture ...The combination of both feature types has also been reported to be effective (Malmasi et ... See full document

... In fact, all compared systems work with hun- dreds of thousands of n-gram features. Training models with such a huge number of features re- quires specific hardware and could be time con- suming. Of course, our model ... See full document

... dialect identification (Malmasi et al., 2016). The combination of transcripts and acoustic fea- tures has also provided good results for dialect identification (Zampieri et ... See full document

... combined with the blended p-grams presence bits kernel. In fact, most of the kernel combinations give better results than each of their components. The best kernel combination is that of the pres- ence bits kernel ... See full document

... Binary verb lemma combined with simple and complex features ([s/c, +bvm]): To validate our assumption regarding the role of surface properties, we tried a combined setup, where the [s/c] setting was used in ... See full document

... Language identification is frequently the initial step in a text processing system that may involve machine translation, semantic understanding, categorization, searching, routing or storage for information ... See full document

... sophisticated feature selection techniques which have been the focus of recent work may result in models which perform better in the ICLE, but which have little or no benefit beyond that particular ...the ... See full document

... based combination obtained ...Ensemble combination boosted our best single-model 2 16 hash size result by ...hashing-based feature spaces. It also shows that model combination can compensate ... See full document

... Before committing to analyzing emotion fea- tures, we want to test whether emotion-loaded words have any impact on the NLI task. The bag- of-words (BoW) representation covers a variety of phenomena, without ... See full document

... The range of feature types used in NLI research raises a further question, namely how the different sources of information are best combined. The most simple solution is to put all features into a single vector. ... See full document

... is language-neutral in that it does not make any assumptions about the language or language type of each ...white space) in each lan- ... See full document

... this feature can be a good approximation of the dependencies feature for low-resourced lan- guages without an accurate ...by language and possibly genre (Liu, ...skip-gram feature space ... See full document

... Feature Selection: After feature extraction the important step in pre-processing of email text classification, is feature selection to construct vector space or bag of words, ... See full document

... Feature Selection and Feature Extract ion for Text Categorization Feature Selection and Feature Extract ion for Text Categorization David D Lewis Center for Information and Language Studies University[.] ... See full document

... follow Wong and Dras (2009) in resolving some un- clear issues from Koppel et al. (2005). Specifically, we use the same list of function words, left unspec- ified in Koppel et al. (2005), that were empirically determined ... See full document

... Previous works on identifying the native lan- guage from texts explored a large variety of features, including lexical and part-of-speech (POS) features (Koppel et al., 2005a), charac- ter n-grams (Ionescu et al., ... See full document

... 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 ... See full document