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As you know, the language of computers and digital tools is the language of zeros and ones. In order to convert this language into human language and vice versa, we need an intermediary program or technology. Our language is composed of letters and numbers, and from it, text and speech emerge. For years, this technology has been invented for mutual understanding between machines and humans.
Natural Language Processing (NLP) is one of the tools of artificial intelligence that is used for communication between humans and machines. The number of applications of this technology in various fields is very high. Areas such as medical research, search engines, business intelligence, and so on, use this tool to achieve their goals. In the following article, we will explain the technology of Natural Language Processing or NLP and discuss more about its advantages and applications. So stay with us.
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What is natural language processing?
Natural Language Processing (NLP), a subset of artificial intelligence, utilizes computational linguistics along with statistical modeling, machine learning, and deep learning to identify, understand, and even generate text and speech. This field has been able to combine the features of generative artificial intelligence, from the communication skills of large language models (LLMs) to the ability of image-generating models to understand requests.
This technology has become an integral part of today’s world and our virtual lives. The numerous services it provides are countless, from search engines and chatbots to voice and digital assistants, as well as translator applications, all in some way use this technology.
The role of machine learning in NLP
Natural language processing uses machine learning tools to understand the structure and meaning of texts. Its role in chatbots, voice assistants, translation software, organizational programs, and apps for converting or scanning audio and texts is undeniable. The use of this tool makes many tasks easier and increases the efficiency and performance speed of individuals, institutions, or organizations.
The method of processing in NLP
Natural language processing uses various methods to empower computers in understanding human natural language. Whether it is spoken or written language, this technology leverages artificial intelligence to receive input from the real world, process it, and understand it by the computer.
To better understand how it works, we can consider a computer similar to a human. Just as we have ears for hearing and eyes for seeing, computers also have their own tools and special programs for reading and collecting audio data.
The input from the eyes and ears in the human body is processed by the brain. Similarly, in a computer, special programs convert inputs into codes that the computer can understand.
The best algorithms
There are two main stages in natural language processing: data preprocessing and algorithm development.
Data preprocessing involves preparing textual data in a way that machines can analyze it. This is done using different methods such as tokenizing information, removing common words, finding synonyms and roots of words, as well as determining the role of words in sentences.
Once data preprocessing is completed, an algorithm is developed for processing it. There are different natural language processing algorithms, with two of the most common ones being:
Rule-based approach: which follows precise linguistic rules formulated by language experts. The first algorithm used in NLP was based on this approach and is still in use.
Machine Learning Algorithms: which use statistical methods for processing and learning based on training data. The main advantage of this algorithm is that it automatically learns from previous data and does not require rule definition.
Why is natural language processing important?
Unstructured data and heavy texts that businesses use need an efficient way to be processed. This is where natural language processing comes to their aid. Data created or stored in human language cannot be effectively analyzed and interpreted, and NLP does this job.
Before the introduction of this efficient tool, machine learning algorithms were unable to recognize and understand ambiguous cases and words that have different meanings. But with advancements in deep learning and machine learning, data analysis has become more extensive.
Similarly, NLP has made interaction with voice assistants and chatbots easier. Instead of using defined special languages for the system, the user can communicate with them using their usual language and dictionary.
Tools and approaches used by NLP
Syntax and semantics are two main tools used in natural language processing. Syntax refers to the arrangement of words in a sentence to create grammatical meaning. Semantics is concerned with the use and meaning behind words.
The techniques used in syntax include structural parsing of sentences, word segmentation, sentence breaking, homograph disambiguation, and word stemming. In semantics, techniques include disambiguation of word meanings, identification of proper nouns, and natural language generation.
For natural language processing, three open-source tools are commonly used: Natural Language Toolkit (NLTK), Gensim, and NLP Architect provided by Intel.
Natural Language Toolkit is a Python module with a dataset and guide. Gensim is a Python library for topic modeling and document indexing. NLP Architect is also a Python library for topologies and deep learning techniques.
The most important uses of NLP
Some of the most important tasks and applications that natural language processing performs include:
Text categorization: In this section, texts are labeled to be placed in a specific category. This categorization is useful for semantic analysis and helps in understanding the hidden emotions and feelings behind a text.
Text extraction: By summarizing the text, important parts of the data can be identified and extracted from it.
Machine translation: In this process, a computer translates a text from one language to another without human intervention.
Natural language generation: natural language processing algorithms are used to analyze unstructured data and automatically generate content based on that data.
The use of NLP in industries
The functions mentioned in various sections of the real world are applicable. For example, it can be used for analyzing customer feedback in businesses, automating customer services, automatic translation, academic research and analysis, analyzing and categorizing medical and treatment records, detecting literary theft, predicting in financial transactions and stocks, talent recruitment in human resources, automating public lawsuits, as well as identifying spam messages and problematic, ambiguous, and deceptive texts.
The advantages of natural language processing
The most important advantage of natural language processing is accelerating the process of computer-human communication. The most direct way to communicate with computers is through coding and programming. When digital and intelligent tools are able to understand human language and communicate directly with them, our work becomes much easier.
In addition, it has other advantages such as: more accurate and effective documentation, using chatbots for customer support in organizations, ability to read and understand long and complex texts, structured and unstructured data analysis, use of personal assistants, analysis of emotions and feelings, better understanding of social media content, organizational research and surveys, and providing deeper insights into the analysis of data that were previously inaccessible due to their large volume.
What are the limitations of NLP?
This efficient tool also has limitations that are often related to changes in natural language. Computers are used to being spoken to in a precise and systematic language, but human language is often not precise and can sometimes be ambiguous and dependent on linguistic structures. And this is effective in the mutual relationship between humans and computers.
Tone, intonation, and some expressions and words are sometimes not recognizable by computers. For example, a computer does not understand sarcasm or irony, and sometimes the meaning and concept of phrases can vary depending on the tone and intonation of the speaker and the context in which the conversation takes place.
consolidation
Natural Language Processing is one of the important subfields in computer science and artificial intelligence, which has significantly simplified the communication between humans and computers today. In this technology, instead of using codes and programming languages, the computer learns to use human natural language to understand texts and speech.
This is done using various algorithms such as rule-based approach or algorithms designed based on machine learning. This tool also has advantages and limitations, which we have detailed in this article.
At BigPro1, we have designed a platform for you to easily tackle your machine learning projects in the shortest time and in the best and easiest way possible, without getting caught up in the complexities of this technology.
Sources: The content of this article has been taken from the websites techtarget, ibm.
