What Deep Learning Does For Natural Language Processing

Deep learning applications for natural language processing have given AI the ability to emulate human perception and cognition and has moved the technology one step closer to perfectly replicating the human abilities.

The use of deep learning or deep neural networks has enabled scientists to create AI that can process and understand the normal human language. This capability, termed as natural language processing, has enabled AI applications to collect, recognize, and classify unstructured data, which had been a challenge until recently. Deep learning applications for natural language processing can help AI read input from various unstructured sources like images, videos, and text. Here are a few ways in which deep learning is enhancing natural language processing:

Deep Learning Applications For Natural Language Processing

1. Text Analytics

Deep learning can enable AI systems to make sense of unstructured data, such as text from different sources. Since a majority of data available is in this format, the use of natural language processing for text recognition can enable data scientists and AI to have access to a wider variety and greater volume of raw data. Access to more data means increased intelligence, which leads to smarter AI and better decision-making capability. Such AI systems can be used for applications like document identification for records management, where an AI program can be trained to classify documents based on their text content. Another such area where text analytics can be applied is spam identification and filtering. This will ensure that unsolicited spam communication is identified and eliminated to maximize productivity.

2. Voice Recognition

Modern speech recognition applications, like the one found in virtual AI assistants like Siri or Cortana, are becoming more adept at picking up audio signals and making sense of them. This is achieved through the use of deep learning and natural language processing, which enables the applications to understand what the speaker says, regardless of accents and imperfections. Similar technology is also being used for transcribing speech to text and for instructing voice-operated systems.

3. Image Captioning

You may have noticed how Facebook displays a textual description of your images or captions when the images fail to load. This is done using a deep learning application for recognizing people and objects in the images, with incredible detail. Such algorithms can determine the number of people in the image, any accessories like glasses or hats, whether the people are facing the camera or not, and any animals that are a part of the picture, among other things. Training these algorithms with more images will only improve their image captioning abilities.

4. Language Translation

Language translators such as Google translate or Microsoft translator use a deep learning application called machine translation that can convert text from one language to its corresponding translation in another. Training the deep learning algorithm in grammatical rules and vocabulary for different languages enables the application to accurately translate between languages with radically different syntaxes, with high accuracy.

Deep learning applications for natural language processing have also proven useful for other problems, such as the natural communication ability of chatbots for conversational commerce and the text prediction capability of smartphone keypads. Natural language processing is enabling AI systems to adopt cognitive techniques that are becoming increasingly akin to those adopted by humans, which will eventually enable AI systems to perform tasks involving direct human interaction.