The History Of Natural Language Processing Dates Back To The 1950s When Alan Turing Published His Famous Paper On The Turing Test, Which Is Now Known As The Benchmark For Machine Intelligence.

The first attempts at computer translation failed, so investors did not want to fund the companies active in this field. After a decade of these efforts, the first positive results emerged, and it was found that the complexity of the language was more than what the researchers initially imagined.

Undoubtedly, the field that was then considered for help in this area was linguistics. However, at that time, there was no linguistic theory that could significantly contribute to the processing of languages.

In 1957, the book Syntactic Structures by American linguist Noam Chomsky, who became the most well-known figure in theoretical linguistics, was published.

What is natural language processing?

Data in the computer world is divided into two groups: structured and unstructured. Structured data is stored in a formatted form in the repositories (databases), and it is easy to exploit them easily.

At the opposite point are unstructured data, which lack a predefined data model (such as videos, images, and texts) or are not organized by default. Unstructured data has a large volume, and due to the high complexity of processing and analysis, it takes a lot of time to extract information from them.

To solve this problem, scientists invented natural language processing technology, which uses unique tools, techniques, and algorithms to quickly process unstructured data such as texts, videos, and audio files.

In this article, we discuss natural language processing. First, we will examine it. We will look at the job market of specialists in this technology, and at the end, we wildiscussne the required skills of a natural language processing specialist.

Natural language processing is an essential need of society.

Natural language processing is a specialized artificial intelligence field rooted in computational linguistics. More precisely, natural language processing refers to using computers to process spoken and written language. The main challenge in this field is to design, build and implement systems that enable communication between machines and natural languages to make this interaction understandable for humans.

It means that the computer can analyze and understand speech or writing produced in the format and structure of a natural language or have the text itself. A model based on this technology can translate languages and use web pages and written databases to answer questions or interact with other machines. The mentioned cases are only a small example of the broad applications of natural language processing.

Its Users decide to enter the world of data science and natural language processing. After entering this field, I realized that analyzing and modeling textual data is not a simple task. Still, as they gain more experience in this field, they become familiar with particular charms, techniques, solutions, and workflows that allow them to use their savings to solve a wide range of problems.

Why do we use natural language processing?

The primary purpose of applying natural language processing is to implement computational hypotheses related to languages ​​using algorithms and data structures available in computer science. Vast language knowledge is needed, and computer science researchers must interact with linguists to achieve this goal; the statistics required to work with natural language can be extracted by processing linguistic information. Applications of natural language processing are divided into two general categories: written and spoken applications.

Writing applications include extracting specific information from a text, translating a text into another language, or finding particular documents in a text database (finding related books in a library). The speech applications of language processing, human question-and-answer systems with computers, automatic customer communication services over the phone, learner training systems, or voice control systems can be mentioned.

What are the limitations of natural language processing?

Natural language processing is one of the most exciting topics in artificial intelligence because it refers to direct communication between humans and machines. If it is fully realized, it will bring notable developments.

Old systems with limited functions, such as SHRDLU, which were related to little and specific words, performed admirably in their time, giving researchers hope in this field. Still, in the face of more severe linguistic challenges, the complexities, and ambiguities of languages, the development of these projects faded quickly.

Usually, the problems related to natural language processing are known as AI-Complete problems because to implement and realize the models properly, designers must have a complete and accurate understanding of the issues and how humans communicate with the problems.

Among the most critical challenges related to natural language processing, the following should be mentioned:

• The need to understand the meanings: For computers to have a correct knowledge of a sentence and to understand the hidden meanings in the sentences, they must get a general understanding of the meaning of the words in the sentence, and only familiarity with the grammar is not enough. For example, Arash didn’t drink the water because the water was cold and Arash didn’t drink the water because the water was hot are identical in terms of grammatical structure and distinguishing whether the words hot and harrowing refer to Arash or water without having prior knowledge of the nature of the Arash. And water is not possible.
• The lack of completeness of grammar: the grammar of any language is not precise enough to understand the role of each of the components of a speech by using grammatical rules. In addition, each language has its grammar. For example, in Farsi, you have the past tense, while in English, it is not like this; instead, you have the future in the past, which functions the same as the past tense in Farsi. However, for an intelligent model, matching the time of two different languages ​​is not an easy task.

How does natural language processing work?

In natural language processing, experts seek to design, implement and discover algorithms to transform unstructured human language data into orderly and comprehensible data for computers. When a text is provided to computers, the computer tries to check all the sentences of the text and uses different algorithms to understand the meaning of those sentences. Sometimes the computer cannot recognize the importance of a particular textual data.

In natural language processing, two main techniques, syntactic analysis and send semantics, are usually used.

• Analyzing syntactic composition in natural language processing
• In natural language processing, syntactic analysis is used to understand the grammatical rules of a language. Syntax refers to the correct arrangement of words next to each other to make a grammatically correct sentence. Computers apply specific techniques and algorithms to a set of words to create grammatically correct sentences. Among these techniques, the following should be mentioned:
• Reduction (Lemmatization): In the above method, different word forms are converted into a single document for more straightforward analysis.
• Morphological segmentation: In the above method, words are converted into smaller units called morphemes.
• Word segmentation: In the above method, a long text is converted into smaller parts (vocabulary).
• Identifying the role of words (Part-of-speech tagging): In the above method, the part of each word in the sentence is determined. For example, a comment is a verb, adjective, subject, object, etc.
• Parsing: In the above method, the grammar of the sentences is evaluated.
• Determining sentences (Sentence breaking): One of the essential principles in natural language processing that must be paid attention to is knowing the correct beginning and end of sentences.
• Stemming: In the above method, experts try to find the simple and essential form of words that differ with changes in their meaning.

Semantic analysis in natural language processing

Semantic analysis is one of the most challenging processes in natural language processing, for which experts have yet to find a comprehensive solution. In the mechanism of semantic analysis, the goal is to identify the correct meaning of a text. Semantic analysis, implementing different algorithms and methods,s is tried to extract the right meaning of the text. Among the most important techniques used in the above process, the following should be mentioned:

• Named entity recognition: In the above method, text parts are placed in predefined groups. For example, specific names of people and places are extracted from the text and compared with keywords belonging to different groups.
• Word sense disambiguation: A word may have many meanings. According to other text parts, the correct meaning is suggested for a comment in the above method.
• Natural language generation: In the above method, new concepts are created from available databases, and new meanings are converted into natural language.

Why is natural language processing a critical need?

Natural language processing allows computers to communicate with humans in their language and listen to human conversations, read texts, analyze received information and identify its essential parts. Today’s intelligent machines have gained the ability to analyze larger volumes of textual data in less time than humans while having a lower error rate or biased perceptions than humans.

Due to the large amount of data produced daily in social networks, experts are forced to use natural language processing to analyze and interpret information.

The second reason for the need for natural language processing is to structure large volumes of unstructured data. Humans speak with so much complexity that sometimes it becomes difficult to understand the meaning of a sentence. In addition, there are many languages ​​in the world, each of which has its own grammar rules.

To be able to write a text in social networks that can be understood in other languages, the algorithms of a social network must have the ability to translate languages ​​correctly and, in addition, to understand and interpret punctuation marks, grammar rules, and even dialects and accents within the texts.

Do Other vital applications of natural language processing include the following:

•  Automatic summarization (shortening a set of data computationally)
• Information extraction (automatic retrieval of information from structured, unstructured, or semi-structured documents)
• Information retrieval (the science of searching for information in a document, searching for the records themselves
• search for metadata that describes the data)
• Machine translation (how to use software to translate text or speech from one language to another)
• Optical recognition of characters (automatic recognition of texts in images and documents and converting them into searchable and editable texts by computer)
• speech recognition (design and implementation of a system that receives speech information)

What skills does a natural language expert need?

Typically, companies are looking to hire people with at least a bachelor’s degree in a computer science or information technology-related field. However, most companies try to hire people with a master’s degree in artificial intelligence. A natural language processing expert’s skill sets are specific compared to other areas of artificial intelligence.

Among these skills, the following should be mentioned:

• Proficiency in Python or Java programming language.
• Mastery of text mining topics.
• Mastering the fundamental concepts of machine learning.
• Getting to know the Tensorflow framework.
• Getting to know the concepts of NoSQL databases.
• Skill in problem-solving and algorithm design, and algorithm implementation.
• Familiarity with text processing algorithms (WordNet).
• Familiarity with NLTK, OpenNlp, and Rweka libraries.
• Mastery of the gate.
• Understanding of REST and Web Service concepts.
• Working experiment thinking algorithms, search, and information extraction.
• Familiarity with PyTorch, Pandas, scikit-learn, and NumPy

What is the state of the job market for natural language processing specialists?

As mentioned, natural language processing is becoming widespread, and almost all the leading companies in the field of using new technologies, especially scientific companies whose field of work is the production and development of strategic and inclusive artificial intelligence products and intelligent assistants, are trying to attract these people. They do.

A natural language processing specialist’s salary depends entirely on their experience, skill level, and the company they choose. Considering that doing this process is not an easy task and you need to master a wide range of skills to do business activities, so we suggest that if you have enough skills in this field and have had successful projects in this field, at least the salary that you offer 11 Consider a million tomans.