Introduction to Deep Learning for NLP
Deep learning for NLP is the part of Artificial Intelligence which is used to help the computer to understand, manipulating and interpreting the human language. NLP deals with the building of computational algorithms that is meant to analyze and represent human languages using machine learning that approaches to algorithmic approaches. We need a wide variety of methods as the text to voice data always vary to a different area, as do the real-time applications(like Speech translation, Speech recognition, Question Answering, Document Summarization, etc.).
Deep Learning is the concept of neural networks. Deep learning methods are helping to solve problems of Natural Language Processing (NLP) which couldn’t be solved using machine learning algorithms. Before the arrival of deep learning, representation of text was built on a basic idea which we called One Hot Word encodings like shown in the below images:
But after the arrival of Deep Learning, we can use methods like word2vec along with some other methods which are now available to represent the text like fastText, Glover, etc.
How does NLP Works?
Below is the explanation for how does the NLP works:
Natural language processing includes many different kinds of methods for translating human language, ranging from machine learning approaches to algorithmic approaches. We need a wide array of methods because the text-voice data always varies to different areas, as do the real-time applications.
Applications for NLP
As neural network helps in various modeling of non-linear processes, so they are being used to solve many existing problems such as evaluating, feature extraction, machine translation, anomaly detection, image classification, computer vision and in many other technologies. So in the same way, deep learning has much application in the field of NLP.
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The major applications of NPL which becomes easier to solve with deep learning are:
- Text Classification and Categorization
- Named Entity Recognition(NER)
- Part of Speech Tagging
- Machine Translation
- Speech Recognition
- Question Answering
- Document Summarization
1. Text Classification and Categorization
Text classification is a very essential part nowadays, to make many applications such as web searching, email spam filtering, language identification, etc. Currently, most of the companies also working on product classification, when they are scrapping data from different websites and lastly making a taxonomy of map data of different sites and providing automatically product classification.
2. Named Entity Recognition (NER)
Named Entity Recognition (NER) first steps for information extraction and classify two entities which are predefined categories such as persons, locations, etc. Many major applications of NER in the real world such as we can find out any tweet containing the name of a person. In the research paper, Neural Architecture for Named Entity Recognition, proposed two methods of NER, the first method is the character-based word from the supervised corpus and second method is unsupervised word representation learned from the unannotated corpora.
Part-of-speech-tagging is having a huge demand in most of the running applications, where the problem is of understating what was the text and text into speech conversion, information extraction and so on. For its implementation, methods (or Features) are available in deep learning like Bidirectional long short term memory, recurrent Neural Network, etc.
4. Machine Translation
Machine Translation is the method to convert text from any source language to any other language. In this, input already consists of many sequences of symbols in a particular language and the computer program has to convert this text to output the required language using symbols available in the required output language.
5. Speech Recognition
Speech recognition is the method where speech\voice of humans is converted to text. A model of language is required to produce human-readable text. The main real-life language model is as follows:
- Creating a transcript for a movie.
- Issuing commands for the radio while driving.
6. Question Answering
This application is used when a subject is given, like a document of text and answers about the questions from the subject is expected. This is one of the most advanced features of NLP using deep learning, where people use a machine to find the answer to a particular question from the given document as input. This application will also enhance automatic chat on websites.
7. Document Summarization
Document Summarization is one of the high demand applications nowadays. In this, the machine itself will create a summary of any big document to small contents. This will help in many cases like:
- Creating Heading of a Document.
- News Articles.
- Create an Abstract of a Document.
There are many benefits of NLP. NLP is the core part of artificial intelligence. Natural Language Processing helps to communicate machine with their own language like robots. The NLP not only helps in communication, but it also helps in solving other real-world problems like converting any written text in the form of computer data. The NLP also helps in making website search results more accurate. For example, if we search for any product like a laptop battery on any e-commerce site like amazon, but the results also include a mobile battery. So NLP will help to cut these errors also during website search.
Conclusion – Deep Learning for NLP
So far we have seen the application of NLP and its benefits of neural-based models such as CNN and RNNs. We can also apply reinforcement learning to get more results according to our requirements. Research on different NLP techniques still going on to get more interaction with the machine.
This is a guide to Deep Learning for NLP. Here we discuss What is Natural Language Processing? how do the NLP works? along with the major applications of NLP which becomes easier to solve with deep learning. You can also go through our other related articles to learn more –