With the advent of artificial intelligence and machine learning, deep learning has emerged as an enabler for almost all kinds of technology and industry. Natural Language Processing (NLP) is one such application of deep learning for which traditional approaches have failed to give desired results. Deep learning based NLP provides way much better performance than the traditional approaches. Keeping in view all these advantages, researchers and practitioners around the world are increasingly focusing on developing deep learning models for NLP applications.
Started with Richard Socher et al's paper in 2011, NLP based deep learning has seen a tremendous growth in the past decade. In this blog post, we will discuss the basics of deep learning in NLP, and its applications.
At its core, deep learning uses Artificial Neural Networks (ANNs), which are mathematical models inspired by the neurons and synapses of the human brain. The deep learning architecture consists of neurons and layers, where each layer refers to a mathematical operation, and each neuron contains a value or parameter that is tuned and manipulated to achieve an objective. The information is represented in sentences, phrases, and words in form of vectors called word embedding.
Word embedding helps in understanding the relationships between words. These embeddings can be derived using mathematical algorithms like neural networks, using data from co-occurring words. Word embeddings are used in various applications like machine translation, question-answering system, and named entity recognition.
The most noteworthy application of deep learning in NLP is machine translation. The Google translation service uses sequence to sequence (Seq2Seq) models to translate text from one language to another. The translation is done by learning just a single model on an entire corpus of paired data. In recent years, Microsoft Translator also switched to deep learning-based approaches for machine translation.
Machine translation has many usages, including automatic technical document translation, travel websites, and chatbots. One can also leverage deep learning models to build text summarization systems that can automatically summarize articles, speeches, and other texts.
Apart from machine translation, deep learning-based NLP is also used for grammar corrections, sentiment analysis, and generating a caption for an image. Deep learning can be also used for building natural language understanding (NLU) models for conversational interfaces (ie, chatbots). NLU models recognize the semantic meaning of the text and are used in natural language dialogue systems.
Deep learning-based NLP is revolutionizing the way computer systems and machines process and understand human language. Deep learning models like seq2seq, word embeddings, and natural language understanding models have opened a plethora of opportunities for researchers, practitioners, and developers to create innovative applications in NLP.