A Comprehensive Guide To Natural Language Processing
Natural language processing (NLP) is a field of computer science, artificial intelligence, and linguistics that studies how humans interact with computers and machines by using natural language. NLP technology and methods are rapidly advancing due to the increased interest in human speech and computer interactions and communications. The advent of big data and the availability of powerful algorithms and computing machines and software have further enhanced the interest in bridging the gaps between the two fields.
In this article, we provide a comprehensive guide to natural language processing. We will discuss the history and evolution of natural language processing, its current applications, and the future of this rapidly developing field.
What Is Natural Language Processing?
Natural language processing (NLP) is a field of computer science, artificial intelligence, and linguistics that studies how humans interact with computers and machines by using natural language. NLP technology and methods are rapidly advancing due to the increased interest in human speech and computer interactions and communications. NLP technology enables computers and machines to understand, interpret, and generate natural language.
NLP is used to analyze and understand natural language, such as text and speech. It can be used to build voice-enabled applications, automated customer service agents, machine-readable data extraction from unstructured text, and automated language translation. It can also be used for sentiment analysis, natural language generation, and natural language understanding.
History of Natural Language Processing
Natural language processing is not a new science; it has been around for quite some time. However, the NLP technology and methods are rapidly advancing due to the greatly increased interest in human speech and computer interactions and communications. The addition of big data and its availability to the greatly enhanced algorithms and powerful computing machines and software has also amplified the interest in connecting the gaps in the fields.
At the lowest level of computers, communication happened only with textual data or words, thanks to millions of ones and zeros. This code produced the logical actions we could understand. Therefore, data scientists and programmers used punch cards to communicate with those first computers seventy years ago. This arduous and manual process was understood by only a small number of people.
Things have changed since the beginning of natural language methods as you can now tell Google Assistant that you like a song, and your device playing the music will respond back in a human-like voice saying that your rating has been saved. Your device will then adapt its algorithm and play that song again if you say it, or it will play that song every time whenever you come across that music station.
When taking a closer look at that interaction, you know that your device is activated when it hears you say something. Therefore, your device understands what you are saying, so your words are executed into actions providing you feedback in well-formed sentences in English which takes no more than several seconds for you to get the feedback.
This complete interaction between you and your machine was made possible by natural language processing along with some other artificial intelligence elements such as deep learning and machine learning. Natural language processing went from linguistic and computational linguistics to this independent study of spoken human language, which plays a vital role in many other computer science fields.
Natural language processing was derived from both linguistics and computational linguistics. As you probably already know, linguistics is the study of language, its phonetics, grammar, and semantics. Classical linguistics is involved in evaluating and devising main rules of language. When it comes to classical linguistics, great progress was made on those traditional methods for semantics and syntax, but the main natural language processing understanding normally resists that clean mathematical formalism.
A linguist is everyone who studies languages more colloquially. When studying natural language processing, you must involve a bit of mathematics as mathematics is the main tool of science. Therefore, natural language processing involves both linguistics and mathematical tools as mathematicians who work on NLP problems focus on the use of discrete mathematical theories and formalisms for their natural language processing studies.
As you already know, natural language processing was derived from the modern study of linguistics and computational linguistics that is the study which uses innumerable tools of computer science to interpret and understand spoken human language. Computational linguistics, in fact, is the broad study of complex computer systems and methods used for generating and understanding natural language. One of the main natural functions of computational linguistics is the testing of language syntax and grammar as proposed by theoretical linguistics.
With the introduction of fast computers and large datasets, new and different things can be easily discovered from large textual datasets just by writing and running some software. Back in the 1990s, statistical machine learning and statistical modeling began to replace those traditional, classical top-down rule-based approaches and techniques used for understanding natural language, primarily because of their better speed, results, and robustness. Therefore, this statistical modeling approach to studying NLP determines the field, but in the future, it may define the field.
To be more precise, data-driven methods and techniques used for interpreting and manipulating natural language have become so popular that they will soon definitely be considered as the mainstream approaches to the overall computational linguistics field. One of the strongest contributing factors to this computational linguistics development is the increased amount of electronically stored data to which these methods can be easily applied. Another contributing factor is a specific disenchantment with the traditional approaches and methods which rely only on those hand-crafted rules mainly due to their brittleness.
This statistical approach to natural language processing is not limited to nor reliant on statistics per-se. However, it includes some advanced machine learning techniques as well as understanding natural language. This requires a huge amount of knowledge about natural language syntax, morphology, pragmatics, and semantics as well as general knowledge about the world we live in. Encoding and acquiring this knowledge about natural language and about the world is one of the main impediments when it comes to developing robust and effective language systems. Just like different statistical methods, natural language processing methods and machine learning techniques promise the automatic acquisition of this knowledge from unannotated and annotated language corpora.
Current Applications of Natural Language Processing
Natural language processing has a wide range of applications in many industries. Below are some of the most popular applications of natural language processing:
• Automated customer service agents: Automated customer service agents are used to answer customer queries and provide assistance. They can be used to quickly and efficiently answer customer queries and provide assistance.
• Voice-enabled applications: Voice-enabled applications are used to interact with users using natural language. They can be used to control smart home devices, provide guidance, and answer questions.
• Machine-readable data extraction: Machine-readable data extraction is used to analyze unstructured text and extract information from it. It can be used to extract data from webpages, emails, and other sources.
• Automated language translation: Automated language translation is used to translate text from one language to another. It can be used to translate webpages, emails, and other documents.
• Sentiment analysis: Sentiment analysis is used to analyze the sentiment of text. It can be used to analyze customer feedback, comments, reviews, and other sources of text.
• Natural language generation: Natural language generation is used to generate natural language from structured data. It can be used to generate reports, summaries, and other text.
• Natural language understanding: Natural language understanding is used to understand the meaning of text. It can be used to extract the intent of a user’s query and generate a response accordingly.
Future of Natural Language Processing
The future of natural language processing is very bright. With the advancements in machine learning and deep learning, natural language processing will be able to understand and interpret natural language better than ever before. As natural language processing technology continues to improve, we will see more applications of natural language processing in many industries. We will also see natural language processing being used to automate more tasks and provide more accurate and efficient services.
Conclusion
Natural language processing is a rapidly developing field of computer science, artificial intelligence, and linguistics that studies how humans interact with computers and machines by using natural language. NLP technology and methods are rapidly advancing due to the increased interest in human speech and computer interactions and communications. NLP technology enables computers and machines to understand, interpret, and generate natural language.
NLP has a wide range of applications in many industries and is being used for automated customer service agents, voice-enabled applications, machine-readable data extraction from unstructured text, automated language translation, sentiment analysis, natural language generation, and natural language understanding.
The future of natural language processing is very bright. With the advancements in machine learning and deep learning, natural language processing will be able to understand and interpret natural language better than ever before. As natural language processing technology continues to improve, we will see more applications of natural language processing in many industries.