To get conclusive results, we have developed the most significant gold standard annotated corpus to date, containing 15 million fully inflected Arabic words. We have developed a systematic framework for spelling and grammar error detection, as well as correction at the word level, based on a bidirectional long short-term memory mechanism and word embedding, in which a polynomial network classifier is at the top of the system. We introduce an approach that investigates employing deep neural network technology for error detection in Arabic text.
Even fewer systems address the detection and correction of erroneous well-formed Arabic words that are either contextually or semantically inconsistent within the text. Our review of previous studies indicates that few Arabic spell-checking research efforts appropriately address the detection and correction of ill-formed words that do not conform to the Arabic morphology system.
There is an increasing demand for applications that can detect and correct Arabic spelling and grammatical errors to improve the quality of Arabic text content and application input. Research on tools for automating the proofreading of Arabic text has received much attention in recent years.
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To facilitate other researchers working in the same domain, we have open-sourced the corpus and code developed for this research. The RCNN model surpasses standard models with 84.98% accuracy for binary classification and 68.56% accuracy for ternary classification. For their assessment, we performed binary and ternary classification studies utilizing another model, namely long short-term memory (LSTM), recurrent convolutional neural network (RCNN) Rule-Based, N-gram, support vector machine, convolutional neural network, and LSTM. The objectives of this work are bi-fold (a) the creation of a human-annotated corpus for the research of sentiment analysis in Urdu and (b) measurement of up-to-date model performance using a corpus. We develop an open-source corpus of 10,008 reviews from 566 online threads on the topics of sports, food, software, politics, and entertainment. In this paper, we develop a deep learning model for the sentiments expressed in this under-resourced language. Most existing studies are focused on popular languages like English, Spanish, Chinese, Japanese, and others, however, limited attention has been paid to Urdu despite having more than 60 million native speakers. Finally, we identify intriguing characteristics of second-order embedding spaces for further research, including much higher density and different semantic interpretations of cosine similarity. Due to variance in the random initializations of word embeddings, utilizing nearest neighbor features from multiple first-order embedding samples can also contribute to downstream performance gains. Additionally, augmenting contextual embeddings with second-order information further improves model performance in some cases. Furthermore, second-order embeddings are able to handle highly heterogeneous data better than first-order representations, though at the cost of some specificity. Surprisingly, we find that nearest neighbor information alone is sufficient to capture most of the performance benefits derived from using pre-trained word embeddings. We then analyze the effects of using second-order embeddings as input features in two deep natural language processing models, for named entity recognition and recognizing textual entailment, as well as a linear model for paraphrase recognition. We introduce second-order vector representations of words, induced from nearest neighborhood topological features in pre-trained contextual word embeddings. Finally, we analyzed the effect of our alignment mechanis. We also show comparable results to several structure-dependent methods. An empirical evaluation of GRA shows higher prediction accuracy (up to 4.6%) of fine-grained sentiment ratings, when compared to other structure-independent baselines. Our novel alignment mechanism allows the RNN to selectively include phrase information in a word-by-word sentence representation, and to do this without awareness of the syntactic structure. In this paper, we propose a structure-independent ‘Gated Representation Alignment’ (GRA) model that blends a phrase- focused Convolutional Neural Network (CNN) approach with sequence-oriented Recurrent Neural Network (RNN). Recent progress on this task has been based on exploiting the grammatical structure of sentences but often this structure is difficult to parse and noisy. The success of sentence classification often depends on understanding both the syntactic and semantic properties of word-phrases.
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