Natural Language Processing using Convolutional Neural Network

Abstract

Convolutional neural networks (CNN) are multi-layer neural networks that are used to learn hierarchical data properties. In recent times, CNN has achieved remarkable advances in the architecture and computation of Natural Language Processing (NLP). The Word2vec technique is considered to introduce Word embeddings, which are used to improve the performance of a variety of Natural Language Processing (NLP) applications. It is a well-known technique for learning word embeddings, which are dense representations of words in a lower-dimensional vector space. Two prominent approaches are used for learning word embeddings, which are dense representations of words in a lower-dimensional vector space, are Continuous Bag-of-Words (CBOW) and Skip-Gram.