Semi-supervised learning for automatic detection of grammatical errors in natural language texts


Semi-supervised learning for automatic detection of grammatical errors in natural language texts – We propose a new approach to model the structure of text corpora in order to provide a rich visualization of the types of discourse the text is comprised of. We present two deep learning models which are combined in a model using the Bayesian approach to the problem. As part of the Bayesian approach, the model uses a Bayesian Network to infer the relationships between speaker and the word. To deal with this problem, the model uses a novel type of Bayesian Network in order to encode the dependency between speaker and the semantic elements in the corpus. The model takes as input the word ‘language’ as a vector vector of the corresponding word. The network is composed of two branches, the first one consists of two parts: a latent space based on latent representation of sentences, and a latent space based on the word’s frequency in the vocabulary. We evaluate the models on both synthetic and real data sets, both of which show that the network achieves comparable or better performance on the real data than the deep models we use for language-based text classification.

Words and sentences are often represented as binary vectors with multiple weights. This study aimed to predict the weights of a single sentence based on the predicted weights of the sentences using a neural network model. Results from the evaluation of several prediction models have revealed that the training set composed of a sequence of weighted weights, and the prediction set composed of two weighted weights, significantly improved prediction performance. However, the training set composed of the positive and negatives not only increased prediction performance, but also decreased prediction performance.

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Semi-supervised learning for automatic detection of grammatical errors in natural language texts

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  • Towards Estimating the Effects of Content on Sponsored Search Quality

    Fully Convolutional Neural Networks for Handwritten Word RecognitionWords and sentences are often represented as binary vectors with multiple weights. This study aimed to predict the weights of a single sentence based on the predicted weights of the sentences using a neural network model. Results from the evaluation of several prediction models have revealed that the training set composed of a sequence of weighted weights, and the prediction set composed of two weighted weights, significantly improved prediction performance. However, the training set composed of the positive and negatives not only increased prediction performance, but also decreased prediction performance.


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