[PDF] Top 20 Native Language Identification on Text and Speech

... In the most 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 developed. In Arabic we observed a number of features that ... See full document

... There are different ways to perform speech synthesis. The choice depends on the task they are used for, but the most widely used method is Concatentive Synthesis, because it generally produces the most ... See full document

... cluding native language, have drawn attention in recent years, via various machine learn- ing approaches utilising mostly lexical fea- ...a text are to some extent influenced by the native ... See full document

... dialect identification, in which the goal is to discrimi- nate among similar languages, language varieties and ...of speech n-grams and function words (Zampieri et ... See full document

... Native Language Identification (NLI) is the task of identifying the native language (L1) of a person based on his/her writing in the second language ...different text ... 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

... The NLI Shared Task 2013 introduced a corpus designed specifically for NLI (Blanchard et al., 2013). Use of a standardized dataset and eval- uation metric allowed for the effective compar- ison of different models, and ... See full document

... Our original interest in NLI began with a curios- ity about the evidence it can provide for the pres- ence of crosslinguistic influence in nonnative speakers’ speech and writing. We believe that NLI strongly ... See full document

... The main variable we used for text selection was the presence of specific L1s. Since the three corpora consider different L1s, we decided to use the L1s present in the largest corpus, COPLE2, as the reference. ... 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 characters and ... See full document

... in text mining tasks such as text categorization, authorship identification or plagiarism detection is to rely on features like words, part-of-speech tags, stems, or some other high-level lin- ... See full document

... the native lan- guage (L1) of a writer based solely on a sample of their writing in a second language ...tive Language Identification (NLI) is an attempt to exploit these errors in order to ... See full document

... Native Language Identification (NLI) is the task of inferring the native language (L1) of an author based on texts written in a second language ...identify language use ... See full document

... Second Language Acquisition (SLA) investigate the multiplex of factors that influence our ability to acquire new languages and chief among these is the role of the learner’s mother ...of Native ... See full document

... In the system designed by Cimino et al. (2013), the authors used a wide set of general-purpose features, designed to be portable across languages, domains, and tasks. This set included features that are lexical (sentence ... See full document

... to Speech and Speech to text are some of the developing techniques in the current ...of speech to text and text to ...or text and analyses them with the existing data on ... See full document

... This work attempts to implement the front end portions for a memory based Malayalam TTS. The TTS system deals with real world data, hence text preprocessing is an important challenge. The sys- tem attempts to ... See full document

... We present the CIC-FBK system, which took part in the Native Language Iden- tification (NLI) Shared Task 2017. Our approach combines features commonly used in previous NLI research, i.e., word n-grams, ... See full document

... This task is mostly solved by machine-learning algorithms, such as SVM (Witten and Frank, 2005). However, the algorithm itself is not the most influential choice for better performance but rather the set of features used ... See full document

... Arabic orthography is very different to English with right-to-left text that uses connective letters. Moreover, this is further complicated due to the presence of word elongation, common ligatures, zero-width ... See full document