Deep Learning Applications for Natural Language Processing

This post will help you to discover deep learning methods, Applications and examples that used in the field of natural language processing, achieving state-of-the-art results for most language problems.

Now advanced deep learning methods are able to exceptional results for specific ML problems, like describing images and translating text from one language to another.

What’s most interesting in this technology? that is a single deep learning model can learn word meaning and perform language tasks.

Many deep learning models have been developed and applied to natural language processing (NLP) to improve, d automate the text analytics functions with NLP features. Moreover, these models and methods are able to produce  superior solutions to understand unstructured text into valuable data and insights.

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Read on to know the deep learning methods are being applied in the field of natural language processing, achieving state-of-the-art results for most language problems.

Generating Captions for Images with NLP Model

Generating Captions for Image NLP Model

Identify and describing the content of an image is a challenging task. It is has to be expressed in natural language which requires a language model too that express how objects in the image are related to each other along with their attributes (visual recognition model).

identify and reading the visual and use semantic elements to generating perfect image captions.

DL(Deep Learning) models can help automatically describe the content of an image using correct and simple English sentences. This is helpful for the visually impaired people to easily access online content.

Generating Captions for Images with NLP Model

Google’s Neural Image Caption Generator system (NIC) is based on a network consisting of a vision CNN followed by a language-generating RNN. The system automatically views images and generates descriptions in simple English easy to understand by common people.

Speech Recognition NLP Model

Deep Learning is being increasingly used to build and train neural networks to understand the audio inputs and perform complex vocabulary and accent speech recognition and separation tasks. In fact, these models and methods are used in signal processing, phonetics, accent speech and word recognition, the core areas of speech recognition.

For example, search engine DL models can be trained to identify each voice to the corresponding users and answer each of the separately.

Speech Recognition NLP Model

Like google search voice searching, CNN-based speech recognition systems can translate raw speech into a text and result on search engine page.

Machine Translation

Like Google Translates is it a best example of Machine Translation with NLP Model.

Machine Translation with NLP Model

Machine translation (MT) is a core task in natural language processing. In this process machine allows to translate languages without human intervention. Deep learning models are being used for neural machine translation.

Deep neural networks (DNN) is better  then traditional MT. DNN offers accurate translation and better performance.

feed-forward neural network (FNNs), Recurrent neural network RNNs ,  recursive auto-encoder (RAE), and long short-term memory (LSTM) are used to train the machine to translate the sentence from the source language to another language with accuracy.

Read more difference between RNN and CNN in Deep Learning.

Question Answering (QA)

Question answering model answer to your query. So, definition based questions, biographical questions, what is and multilingual questions among other types of questions asked in natural languages are answered by such systems.

Developing a fully functional question answering system has been one of the challenges faced by researchers in the DL segment.

The deep learning algorithms have developed model for text to speak and image classification in the past but that is not able to solve the tasks that involve logical reasoning. However, in recent times, deep learning techniques are developing and improving the performance and accuracy of these QA systems.

Recurrent neural network RNNs models, for instance: its able to correctly answer paragraph-length questions where traditional model are not able to do this.

Document Summarization or Data mangement

Day by day, increasing volume of data.  To mange this data need a model for Document Summarization.  The latest sequence-to-sequence models have made it easy for  Deep learning DL experts to develop good text summarization models.

There are two types of document summarization as follow.

  1. Extractive
  2. Abstractive

Steps for document summarization model

  • First encoder RNN reads the source text, produce a sequence of encoder hidden states.
  • Then in 2nd steps, the decoder RNN receives the previous word of the summary as the input. It uses this input data to update the decoder hidden state (the context vector).
  • Finally, the context vector and the decoder hidden state gives the output. This sequence-to-sequence model where the decoder is able to generate words in any order is a powerful solution to abstractive summarization.

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Conclusion of DL NLP:

The field of language processing models shifting from statistical language processing to deep learning methods and neural networks. Because DL models giving a superior performance on complex NLP tasks.

Thus, deep learning models seem like a good approach for accomplishing NLP tasks that require a deep understanding of the text, namely text classification, machine translation, question answering, summarization, and natural language inference among others.

This post best for role of DL models and methods in natural language processing.

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