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

The first attempts at computer-based translation failed, as most investors were reluctant to fund the companies needed. A decade after these efforts, the first positive results emerged, and it turned out that the complexity of the language was greater than previously thought by researchers. Undoubtedly, the field that was then considered for help in this field was linguistics.

However, there was no linguistic theory that could significantly contribute to the processing of languages at that time. Then, in 1957, the book Syntactic Structures by the American linguist Noam Chomsky was published, becoming the most well-known figure in theoretical linguistics.

What is natural language processing?

Data in the computer world is divided into two groups: structured and non-structured. The structured data is stored in formats inside repositories (databases) and can be easily exploited. In contrast, unstructured data lacks a predefined data model (such as movies, images, and text) or is not organized by default.

Unstructured data is large and requires a lot of time to extract information due to the high complexity of processing and analysis.

To solve this problem, scientists have developed natural language processing technology that allows the processing of unstructured data such as text, video, and audio files more quickly with the help of unique tools, techniques, and algorithms.

Natural language processing is an important need of Iranian society

Natural language processing is a specialized field in artificial intelligence rooted in computational linguistics. The main challenge in this area is the design, construction, and implementation of systems that enable communication between machines and natural languages. In such a way that this interaction is understandable to humans.

More specifically, natural language processing refers to using a computer to process spoken and written language. This means that the computer can analyze and understand speech or writing produced in the form and structure of a natural language or produce the text itself.

The technology-based model can translate languages, use web pages and written databases to answer questions or interact with other machines. These are just a few of the many uses for natural language processing.

Why use natural language processing?

The main purpose of using natural language processing is to implement computational hypotheses related to languages ​​using algorithms and data structures in computer science. Achieving this goal requires a broad knowledge of the language, and computer science researchers need to interact with linguists.

By processing linguistic information, the statistics needed to work with natural language can be extracted. Natural language processing applications fall into two general categories: written applications and spoken applications.

Writing applications include extracting specific information from a text, translating a text into another language, or finding specific documents in a written database (finding related books in a library).

Speech applications of language processing include human-to-computer Q&A systems, automated telephone communication services, learner training systems, or voice control systems.

What are the limitations of natural language processing?

Natural language processing is one of the fascinating topics in artificial intelligence because it refers to the direct connection between man and machine. If fully realized, it will bring about amazing changes.

Older systems with limited functionality, such as SHRDLU, which were associated with limited and specific terms, performed admirably in their time, promising researchers in the field, but in the face of more serious linguistic challenges, linguistic complexities, and ambiguities, these projects flourished. It faded quickly.

Problems related to natural language processing are commonly known as AI-Complete problems. Designers must have a thorough and accurate understanding of the issues and how humans relate to problems to implement models correctly.

 Among the most important challenges related to natural language processing are the following:

• Need to understand the meanings: To properly understand a sentence and understand the meanings hidden in a sentence, computers must gain a general understanding of the meaning of the words in the sentence, and familiarity with grammar alone is not enough. For example, Arash did not drink water because it was cold water, and Arash did not drink water because it was hot water. However, they are grammatically similar in structure and recognize whether hot and cold words refer to Arash or water without prior knowledge of the nature of Arash. And water is not possible.
• Lack of comprehensiveness of grammars: The grammar of any language is not accurate enough to understand the role of each component of a language using grammatical rules. In addition, each language has its own specific grammar. For example, in Persian, you have an obligatory past tense, while in English, this is not the case, and instead, you have a future in the past that functions as an obligatory past participle in Persian. However, matching the timing of two different languages ​​is not an easy task for an intelligent model.

How does natural language processing work?

In natural language processing, experts seek to design, implement, and discover algorithms that convert nonstructured human language data into regular, comprehensible data for computers.

When text is provided to computers, the computer tries to examine all the sentences in the text and uses different algorithms to understand the meaning of those sentences.

Sometimes a computer cannot recognize the meaning of a particular textual data. Therefore, in natural language processing, two main techniques of syntactic analysis and semantic analysis are commonly used.

Syntactic composition analysis in natural language processing

Syntactic syntax refers to the correct arrangement of words together to make a grammatically correct sentence. In natural language processing, syntactic analysis is used to understand the grammatical rules of a language. Computers apply special techniques and algorithms to a set of words to create grammatically correct sentences.

These techniques include the following:

• Lemmatization: In the above method, for easier analysis, different word shapes are converted into a single form.
• Morphological segmentation: In the above method, words are transformed into smaller units called a morpheme.
• Word segmentation: A long text is divided into smaller parts (words).
• Part-of-speech tagging: ‌ In the above method, the role of each word in the sentence is specified. For example, a word is a verb, an adjective, a subject, an object, and so on.
• Parsing: ‌ In the above method, the grammar of sentences is evaluated.
• Sentence breaking: One of the most important principles that must be considered in natural language processing is knowing the correct beginning and end of sentences.
• Stemming: In the above method, experts try to find the simple and basic words that differ with changes in their meaning.

Semantic analysis in natural language processing

In the mechanism of semantic analysis, the goal is to identify the true meaning of a text. Semantic analysis is one of the most difficult processes in natural language processing that experts have not yet been able to find a comprehensive solution to. In semantic analysis, it tries to extract the correct meaning of the text by implementing different algorithms and methods.

The most important techniques used in the above method are the following:

• Named entity recognition: In the above method, parts of the text 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 can have many meanings. According to other parts of the text, the correct meaning for a word is suggested in the above method.
• Natural language generation: In the above method, new concepts are created from the available databases, and new meanings are converted into natural language.

Why is natural language processing one of the most important needs?

Natural language processing allows computers to communicate with humans in their own language, listen to humans speak, read texts, analyze incoming information, and identify important parts. Today’s smart machines have the ability to analyze larger volumes of textual data in less time than humans while having lower error rates or biased perceptions than humans.

Due to the large amount of data generated daily on social networks, professionals are forced to use natural language processing to analyze and interpret information. The second reason for the need to process natural language is to structure large volumes of unstructured data.

Humans speak with such complexity that it is sometimes difficult to understand the meaning of a sentence. In addition, there are many languages ​​in the world, each with its own grammatical rules.

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

Other important applications of natural language processing include the following:

•  Automatic summarization (shortening a set of data is computational)
• 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 documents 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 character recognition (automatic detection of texts in images, documents and their conversion into computer searchable and editable texts)
• 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 attract people who have at least a bachelor’s degree in a field related to computer science or information technology. Most companies, however, try to hire people with a master’s degree in artificial intelligence. Compared to other areas of artificial intelligence, the set of skills that a natural language processing expert needs is clear.

These skills include the following:

• Fluency in Python or Java programming language.
• Mastery of Text Mining topics.
• Mastery of the basic concepts of machine learning.
• Familiarity with the Tensorflow framework.
• Familiarity with the concepts of NoSQL databases.
• Skills in problem-solving and algorithm design and implementation of algorithms.
• Familiarity with word processing algorithms (WordNet).
• Familiarity with NLTK, OpenNlp, and Rweka libraries.
• Mastery of the gate.
• Familiarity with the concepts of REST and Web Service.
• Work experience in ranking, search, and information extraction algorithms.
• Familiarity with the base torch, pandas, scikit-learn, and NumPy

What is the labor market situation of natural language processing specialists?

As mentioned, natural language processing is becoming ubiquitous. Almost all leading companies in the field of using new technologies, especially knowledge-based companies whose field of work is the production and development of strategic and comprehensive products of artificial intelligence and intelligent assistants, are attracted to these people. They do.

The amount of salary a natural language processing specialist receives depends entirely on the experience, skill level, and company they have chosen.

Given that this process is not an easy task and you need to master a wide range of skills to do business, we suggest that if you have sufficient skills in this field and have successful projects in this field, the minimum salary you offer 11. Consider a million tomans.