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What is Natural Language Processing (NLP)?
In a tutorial , we are going to explore natural language processing including the definition of basic concepts, looking at its applications and methods. So, let’s start with the definitions and applications of natural language processing.
Humans usually communicate with each other through language and in the form of speech or writing. With the advancement of technology and the widespread use of intelligent machines in every aspect of our daily lives such as mobile phones and computers, the lack of communication between machines and humans is deeply felt. Transferring the concepts and thoughts from human to machine or vice versa is an astonishing field that has gained a lot of fans these days, but it can be said that we are far from the desired level. The purpose of natural language processing is to understand and make sense of human languages by machines. This means the machine learns the language like a newborn and then uses it to communicate. Nowadays, NLP tools play an important role in our daily lives, such as:
· Voice assistants: Siri, Google Assistant, and Cortana
· Translation machines such as Google Translate (English), Torjoman (Persian)
· YouTube Auto Subtitle Service
· Gmail Grammar Correction Service
· Automatic text correction and normalization systems
Common methods in NLP
If we consider machine learning as a tool, most of its methods and algorithms can also be used in natural language processing. Machine learning is useful for any type of data; however, the data should be converted to an appropriate format, which could be in different forms: text, audio, or image. Here is a list of some tasks that can be done in the field of natural language processing.
A classification model places examples into one of two or more categories. Suppose we want to classify email into spam (email we don’t want) and non-spam (email we want) or in advanced cases, recognizing the contents of a commercial contract as legal/illegal.
The purpose of clustering is to collect similar data, or in other words, dividing the data into some groups in which that data points in the same group are more similar to each other.
Another method called Topic Modeling can also be used. Assume that we want to analyze texts in Google’s database so that the same topics belong to one category.
Every text has a message. This message contains positive/negative emotions or sentiments. In sentiment analysis, the goal is to determine if the person who wrote a sentence or text about an entity has a positive or negative (or neutral) opinion about that entity and try to interpret the sentiments behind it.
Consider digital libraries that want to provide better service to their users. The purpose is to use the user’s previous history to make more appropriate suggestions. So that if a person has already read a few books with historical content about World War and has recently ordered several epic books, in the future he will be offered books about the life of epic characters in different wars. Such a service requires a general understanding of the content of each book.
Imagine instead of reading a thousand-page book in a month, a machine will give you an automatic summary in a short time.
Application of NLP in various fields
Natural language processing can be effective in growing and improving businesses. For example, in insurance companies with many customers, managing complaints is a difficult and repetitive task. Usually, a part of it is done manually and another part is done online. Therefore, speed and accuracy are affected. In such a situation, if a smart assistant (chat-bot) is designed, it could help to automate some parts of the workflow. Of course, it is not limited to business applications and has a broad range.
Take Twitter as another example. Millions of tweets are shared daily on Twitter by users around the world. Twitter has become an important source for sentiment analysis due to the expression of different user’s emotions. These days, most people choose Twitter as a suitable platform for sharing their opinion about various phenomena or occasions around them.
For instance, in the presidential election, each person publishes their opinions in the form of tweets and that is how Twitter reflects an important part of the attitude of people in society. A political current or person can immediately measure the success of their action via Twitter. The only thing to do is to analyze a large part of the tweets related to that topic, and the result of the analyzing process is a good criterion for measuring public opinion. It can be concluded that, as different people with different interests are members of Twitter and express their opinions freely, a study on Twitter around a specific topic (whether political, economic, etc.) can predict the positive or negative opinions of people in society. Likewise, political currents and economic campaigns with the help of this method eliminate the need to prepare a questionnaire and field survey of people’s opinions to a significant level.
General approaches to NLP tasks
1) Rule-based Systems
This method is based on generating different rules with the help of regular expressions. It is a good method for simple problems such as extracting national code from a text, but since most natural language processing tasks are difficult, if we want to use this method, we must generate many rules and be careful about the exceptions each time. Also, the system cannot learn automatically and everything is manual, so it practically fails because it is not dynamic.
2) Data Mining
This method is more flexible in comparison with Rule-based Systems and can learn automatically. As an example, detecting spam emails requires thousands of rules but can be easily solved with the help of data mining and classification methods. Of course, the main challenge in data mining is the production and selection of features, all of which are manual and considered a disadvantage. Another point worth mentioning is when it comes to things like translation machines, data mining methods are not efficient.
3) Deep Learning
Nowadays, a high percentage of articles focus on these methods, and also active companies in the field of natural language processing, use deep learning for their complicated services such as language model production, language recognition, and intelligent assistant. The advantage of this method is the elimination of the feature extraction and production step and making it automated. Of course, this method does not work well in every task, and sometimes data mining methods work better. For example, in some subject areas, it is better to use data mining algorithms to analyze emotions (sentiment analysis).
In this article, we have explained the basics of natural language and an overview has been obtained. In the next article, we will go for the installation and introduction of tools.
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