[PDF] Top 20 Native Language Identification Using a Mixture of Character and Word N grams
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Native Language Identification Using a Mixture of Character and Word N grams
... Our implemented method is useful in the native language identification of essays (outperforming the baseline F1-score of 0.710), it does not per- form well on speech transcriptions (whose base- line ... See full document
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Native Language Identification: a Simple n gram Based Approach
... by using lower level lexical and shallow syntactic ...by using the fine grained POS tags and the coarse grained POS ...classifier using the training dataset, and using the model we tested the ... See full document
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CIC FBK Approach to Native Language Identification
... the native lan- guage from texts explored a large variety of features, including lexical and part-of-speech (POS) features (Koppel et ...ter n-grams (Ionescu et ...used word ... See full document
8
Using N gram and Word Network Features for Native Language Identification
... one word in the set of repre- sentative ...document’s word net- work, or as the local clustering coefficient vector of these words in the word network, or as the coreness vector of the words (and so ... See full document
9
Simple But Not Naïve: Fine Grained Arabic Dialect Identification Using Only N Grams
... of word n- grams, character n-grams, language models per di- alect, and sentence probabilities given by the lan- guage models, achieving an accuracy of ... See full document
5
Exploring Word Class N grams to Measure Language Development in Children
... establish language proficiency, clinical re- searchers and practitioners rely on a variety of measures, such as number of different words, type-token ratio, distribution of part-of-speech tags, and mean length of ... See full document
9
Native Language Identification using Recurring n grams – Investigating Abstraction and Domain Dependence
... rekurrenter N-Gramme aller Längen als Features für das Trainieren eines ...oberflächenbasierten N-Grammen, untersuchen wir zwei Stufen der Abstraktion unter Verwendung von ...: Native Language ... See full document
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Native Language Identification: A Key N gram Category Approach
... key n-gram lists. Removing all words occurring in the prompts from the n- grams list would remove a number of high fre- quency words that may not be prompt-based ...less. N- grams were ... See full document
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A study of N gram and Embedding Representations for Native Language Identification
... that word level features still are the most predictive ones as a single group ...classification using simpler feature rep- resentations based on words, characters and POS ...why n-grams may ... See full document
9
Do Characters Abuse More Than Words?
... user language evolves either consciously or unconsciously based on standards and guidelines imposed by media companies that users must adhere to, in conjunc- tion with regular expressions and blacklists, to catch ... See full document
5
Native Language Identification using Phonetic Algorithms
... We had a closer look at the errors that our sys- tem makes. Figure 2 shows a confusion matrix for the best setting using character and DMETA fea- tures. The table shows that the main weak point of the ... See full document
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A Shallow Neural Network for Native Language Identification with Character N grams
... with character n-grams features could effectively identify the native lan- guage (L1) of the ...that character n- grams mostly works for any style-based classifica- tion ... See full document
6
Complex Word Identification Using Character n grams
... the n-gram lengths, combination “24” is useful, although mostly for ...the n-gram ...eign” language over another was observed – the best results are rather similar for both “external” ...the ... See full document
8
The Power of Character N grams in Native Language Identification
... the character n-gram classifier, as well as the output of a com- bined character n-gram and a simple neural net- work (CBOW, see Section ... See full document
8
Stance Detection in Fake News A Combined Feature Representation
... Recently, many phenomena appeared and spread in the Internet, especially with the huge propaga- tion of information and the growth of social net- works. Some of these phenomena are fake news, rumors and misinformation. ... See full document
6
chrF++: words helping character n grams
... individual n-grams, several different experiments have been carried out, involving different character n-gram weights, combining character and word n-grams with ... See full document
7
Local Histograms of Character N grams for Authorship Attribution
... We have described the use of local histograms (LH) over character n-grams for AA. LHs are enriched histogram representations that preserve sequential information in documents (in terms of the ... See full document
11
Investigating Sub Word Embedding Strategies for the Morphologically Rich and Free Phrase Order Hungarian
... 2). Word embeddings obtain such semantic and syntactic capabilities by matching the words to their observed contexts (or vice ...the word vector table is the vocabu- lary size times the embedding dimension, ... See full document
7
Detection of Steganographic Techniques on Twitter
... Due to the expensive nature of generating data with a human judge, and to assess the value of the human in-the-loop, we also automatically gen- erated 1M stego tweets, by embedding data in 1000 tweets for each of the ... See full document
6
Converting System of PhoneticsTranscriptionstoMyanmarText Using N-Grams Language Models
... for language model is n- gram language modelling. N-gram language models are based on statistical of how likely words are to follow each ...In Language Modelling, the system ... See full document
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