Natural language processing with neural networks

Abstract

The rapid growth of language technologies has rendered a revolution in the era of Natural Language Processing (NLP). There are enormous real world applications of Neural Network processing of NLP ranging from simplest task of text classification to hard task of question answering. The automation of various language processing tasks outperforms the human abilities as the large volume of text on Internet makes it difficult to analyze it manually. The training of neural networks to learn trends in the upcoming dialogues on the web resolves many NLP applications. This paper highlights eleven real world applications of NLP using Neural Networks.

A New Method for Commercial-Scale Water Purification Selection Using Linguistic Neural Networks

“…This makes FFNNs a relatively simple type of neural network compared to others such as RNNs and CNNs. Numerous applications have made use of FFNNs, such as natural language processing [10,11], image [7,8] and speech recognition [9], and financial forecasting [31]. They have been among the most successful learning algorithms and have been the basis for many other types of neural networks.…”

“…In the field of pattern recognition, NNs have been shown to outperform classical statistical methods, particularly when dealing with complex and high-dimensional data. In prediction and classification tasks, NNs have also been successful, achieving high accuracy rates in fields such as image [6,7] and speech recognition [8], natural language processing [9,10], and sentiment analysis [11,12]. Overall, while classical statistical methods remain useful in many applications, NNs have become a popular and powerful tool for solving complex problems in various fields.…”

A neural network-based automatic summarization for the minutes of local assemblies

“…Works on the "neural network" for natural language problems already exist [9]- [12]. The term "neural network" is used because the computational model is inspired from the biological neurons in the brain, and it has several promising characteristics in computation as discussed layer.…”

“…In [ 1] [ 12], the neural network for the generation and detection of document categories are shown. In [13] [14], the neural networks for the text analysis are shown.…”

Development of Prototype Morphological Analyzer for he South Indian Language of Kannada

“…This serves as an efficient tool for the preprocessing activities of Kannada document digitization and content management. The performance of the existing OCRs for Kannada can be improved by modifying the morph analyzer to a spell checker, thereby correcting the mistakes, which the OCR has incurred [4]. As it also serves as a stemmer, Kannada document summarization and classification is made possible, which has not been attempted yet.…”