What is Natural Language Processing or NLP?

What is Natural Language Processing or NLP? Why is natural language processing important? Who is a natural language processing expert or engineer? How much does he earn? How is the NLP Specialist marketer? What is the role of the Python programming language and machine learning in teaching human language to machines and software?

The questions asked are very hot topics in the field of technology and artificial intelligence. Imagine if the machine (software) could understand English or French or any other language exactly and completely? If we pay attention to our surroundings, we might find machines that understand our language and talk to us.

If you have an iPhone, you must be familiar with Siri. I have a friend who asks Siri to tell him Jack. He considers Siri as his friend. Of course, Siri still can’t fully understand all of her words like a human because our language is more complicated than we think. But it doesn’t matter. Siri has the power to learn and improve from interacting with my friends.

Teaching machines human language has many applications. Siri is just a small example. It is made using the artificial intelligence of humanoid robots. Robots that can answer journalists’ questions! Without natural language processing and progress in that field, it would not be possible to build humanoid robots which will be an integral part of our human lives in the not-so-distant future.

In this article, I want to introduce you to one of the most interesting and at the same time the most lucrative subfields of artificial intelligence (i.e. NLP) and answer the questions that started this article.

What is natural language processing?

Natural language is the language with which humans communicate with each other, verbally and in writing. Humans convey their intentions to others with natural language. I have now written this text for you in natural language. Note that natural language is not a specific language, such as English. A language that is a set of words and terms and has specific rules (grammar or grammar). Natural language, human language, has other characteristics.

One of the most important features of natural language is its dynamics. Language changes over time. New words and terms are introduced into the language and some words are not used in conversations or texts after a while. Humans learn language. You and I first learned the language from our parents and environment and then at school. Writing each language has its own rules. Writing and understanding any text (scientific, play, review, novel, short story, product review, etc.) also has its own rules, words, and terms. Therefore, natural language should not only be learned; it should be studied.

Linguistics is a science that studies and examines natural language. Linguistics has many sub-disciplines. One of its interdisciplinary subfields is Computational Linguistics. In computational linguistics, experts seek to find computer models (computer models) for natural language. Natural language processing is another interdisciplinary subfield of linguistics in which experts from the 3 fields of linguistics, computer science, and artificial intelligence seek to find a way for human-machine interaction through human natural language.

“NLP enables computers to understand natural language as humans do.”

How do Python and machine learning process natural languages?

It may be claimed that in this world, before artificial intelligence and the powerful language of Python and machine learning, there were humans who could only learn and understand natural language. But now, machine learning and deep learning have given a non-living being, an algorithm, the possibility to learn natural languages. In the natural language processing process, in 2 steps and using technologies, the computer is taught to receive and process data, which may be text or speech, to understand it and produce the desired output (which may be an answer, analysis, or any detail, the other is text or words) to deliver.

Natural language processing steps

1. Data Preprocessing

NLP starts with an unstructured text. Before teaching the natural language to the machine (algorithm), the natural language, i.e. the text or audio that the machine is supposed to understand, must be changed to the machine language, i.e. structured text, which is first converted to text by the speech to text algorithm. The data (input) that is available to the machine must be in a format that the machine can process.

2. Algorithm Development

Natural language processing is done by algorithms. Therefore, the algorithm of the processor should be made based on rules and determine for him how to do the processing work. This is where artificial intelligence and its sub-disciplines, namely machine learning and deep learning, help natural language processing experts to train the algorithm.

Natural language processing technologies

Natural language processing engineers perform the mentioned steps by employing various technologies, techniques and tools. Two types of approaches or analysis may be used to structure the data and also to train the algorithm: syntactic or semantic. Experts choose the approach and tools according to the application and the information they want to get from natural language processing. However, the following 5 technologies are definitely used, and the following are some of the main foundations of natural language processing:

• Tokenization: First, the unstructured data must be broken down into its smallest constituent units (words). Each word is a token for the machine. For example, the previous sentence has 7 words, i.e. 7 tokens.
• Stop Words: It is necessary to include words, such as conjunctions or documentary verbs (like is), which are not important information of the text; be deleted
• Stemming or Lemmatization: Now the machine has to find the lexical root (stem) of each word, that is, it has to remove the suffixes and prefixes of the words. For example, the roots of the best and the best and the best are obtained by removing the most and the most. Of course, the point here is that the roots of all words cannot be obtained by removing suffixes or prefixes (for example, the roots of the two words “doors” and “door” are not the same). So, for some words, the machine must find its original meaning (Lemma), that is, the meaning that is intended for that word in the dictionary.
• Part of Speech Tagging: Now the grammatical role of each word (code) in the sentence should be determined, whether it is a verb or an adjective or….
• Named Entity Recognition: When you and I hear and read the name of Paris, what comes to mind about these two names? The capital of France. In order to understand natural language, the algorithm must know and understand proper nouns, declarations, and general information.

Python libraries for natural language processing

It is not an exaggeration to say that the Python programming language serves artificial intelligence. It is Python that, along with other sciences and technologies, has made machine learning and deep learning possible. The path of learning machine learning and deep learning begins with learning Python. You might think that you must be an expert in machine learning to process natural language. But this notion is wrong.

If someone has learned the Python programming language, with the help of NLTK (Natural Language Toolkit), which is a Python package for natural language processing; it is easily able to process the text it wants, as needed, and output its results in the form of a graph or chart (visualized). It is an open-source model package for natural language processing, which has many online educational resources to learn it.

Of course, in addition to that package, Python has very powerful libraries with which some natural language processing technologies can be implemented. The Gensim library is for building and developing semantic natural language processing models. Intel NLP Architect is another library for topology and deep learning techniques that enhance natural language processing.

Why is natural language processing important?

To answer the question why natural language processing is important, we need to look at its applications in various fields. Natural language processing is not only useful and practical for understanding the structure of language as well as human interactions, making robots and virtual assistants (Virtual Assistants) like Alexa or even chatbots. Businesses and commercial enterprises can use natural language processing to their advantage.

Because natural language processing and algorithms that understand natural language can understand and analyze text data (opinions and comments) that businesses have collected from social networks or other platforms. As a result, the necessary data to know and predict customer behavior is provided to the business.

The most important applications of NLP

• Text Extraction or Summarization: Natural language processing algorithms can process the text, extract important information or deliver a summary of the text. The machine may be asked to search for a specific keyword in the text and extract only those parts of the text in which the keyword is used.
• Text Classification and Sentiment Analysis: Let me give an example to make this application clear. Imagine you have a very large business with millions of followers on social networks. There is a lot of talk about your brand and products on the internet. Now, if your business wants to know whether users’ opinions about the latest product are positive or negative, it can do so by categorizing the text (data) by defining specific tags for the machine. Of course, businesses also use sentiment analysis to supplement data obtained from text to understand how a user who wrote positively about a brand or product on social media felt; He was joking, teasing or serious.
• Machine Translation: It is not an exaggeration to say that all Internet users have had the experience of using Google Translate. Advances in natural language processing and training algorithms that can better understand the context and topic of each text will greatly help improve machine translations.

Who is an NLP specialist?

A natural language processing expert, a natural language processing engineer, a machine learning expert or engineer who specializes in natural language processing and deep learning specialists are all specialists who have the necessary knowledge and skills to carry out natural language processing projects. The common feature of all those experts is that they have the necessary skills to use natural language processing tools, techniques and technologies and can train an algorithm and build a machine (program) that understands human language.

Of course, the purpose and the task for which the training and development of the natural language processing algorithm is required will determine the type of expert who should work on the project. For example, a business may want to design a sentiment analysis model for itself. A data scientist familiar with NLP is ideal for this business. Because the company wants someone who knows data collection and analysis as well as machine learning. For some projects, it may be necessary for a natural language processing engineer to be fully proficient in linguistics or computational linguistics, or even have a university education in those fields.

How is the income and job market of natural language processing engineer in the world?

Well, we come to the last important question about NLP: how much does an engineer or natural language processing specialist earn? Are there significant job opportunities in the world for this specialist? If you search natural language processing engineer jobs on LinkedIn, you will find 29,000 jobs in the US and 1,000 jobs in Canada. The average annual salary of an NLP specialist in the US is $112,000, in the UK it is £56,000 and in Canada it is $99,000 Canadian.