Natural Language Processing

“Human language is highly ambiguous … It is also ever-changing and evolving. People are great at producing and understanding language and can express, perceive, and interpret very elaborate and nuanced meanings. At the same time, while we humans are great users of language, we are also very poor at formally understanding and describing the rules that govern language.”

What is NLP?

Natural Language Processing, abbreviated as NLP is the automated manipulation of natural language by software, such as speech and text. NLP aims to create robots that interpret and respond to text or voice input in the same manner that people do — and respond with text or speech of their own.

NLP is a linguistics-based technology that has been around for more than 50 years. Medical research, search engines, and corporate intelligence are just a few of the real-world uses.

Why NLP?

As human language is riddled with ambiguities, developing software that properly interprets the intended meaning of text or speech input is incredibly tough. Homonyms, homophones, sarcasm, idioms, metaphors, grammar and usage exceptions, sentence structure variations — these are just a few of the irregularities of human language that take humans years to learn, but that programmers must teach natural language-driven applications to recognize accurately from the start if those applications are to be useful. Hence NLP plays an important role.

How does it work?

Natural language processing is divided into two stages: data pretreatment and algorithm development. Data preprocessing is the process of preparing and “cleaning” text data so that machines can examine it. Preprocessing transforms data into usable form and emphasizes textual elements that an algorithm can use. An algorithm is created to process the data once it has been preprocessed. Natural language processing algorithms come in a variety of forms.

Computational linguistics — rule-based human language modeling — is coupled with statistical, machine learning, and deep learning techniques in NLP. These technologies, when used together, allow computers to analyze human language in the form of text or speech data and ‘understand’ its full meaning, along with the speaker’s or writer’s intention and mood.

The Python programming language offers a large selection of tools and packages for carrying out specific NLP tasks. Many of them are contained in the Natural Language Toolkit, or NLTK, an open-source collection of libraries, applications, and instructional resources for developing NLP algorithms.

Where is it used?

Although natural language processing is still evolving, it is already being used in a variety of ways. You’ll be exposed to natural language processing most of the time without even recognizing it.

NLP is frequently used in the background of the products and applications we use on a daily basis, assisting companies in improving user experiences. The major tasks performed using NLP include:

1. Translation: It is the process of determining the best approach to represent a phrase or a word in a foreign language. Perhaps the best example is Google’s translation, a service that has steadily improved over time.
2. Speech recognition: This type of NLP application can be seen in smartphones and smart homes. Simply saying ‘call Peter’ causes the device to identify the phrase and dial Peter’s number. Alexa and Siri are two popular examples of this.
3. Sentiment analysis: It detects a phrase’s implied emotional tone. For instance, if a text reports a stock market decline, the program can predict that it is a negative text. If, on the other hand, a sentence mentions a party and who is invited, its meaning can be interpreted positively.
4. Spam detection: Take, for example, Gmail. Thanks to an NLP process called keyword extraction, emails are automatically classified as Promotions, Social, Primary, or Spam. Machines automatically learn which category to allocate emails by “reading” terms in subject lines and correlating them with specified tags.
5. Chatbots: They are artificial intelligence algorithms that converse with people. When someone needs to buy anything from an online store and has specific inquiries regarding the item, the responses they receive are almost certainly generated by a machine.

NLP and IBM

IBM has advanced in the artificial intelligence field by pioneering NLP-driven products and services that enable organizations to automate their intricate business processes while acquiring important business insights. These tools include the Watson Natural Language Understanding, Watson Assistant along with Watson Discovery.

The transition from NLP to NLU has several significant ramifications for both organizations and consumers. Consider the impact of an algorithm that can comprehend the meaning and nuance of human language in a variety of settings, including medical, law, and the classroom. As the amount of unstructured data grows rapidly, we will benefit from computers’ never-ending capacity to assist us in making sense of it all.

Benefits and Challenges of NLP

The fundamental advantage of NLP is that it enhances the way people and computers interact. The simplest approach to controlling a computer is to use code, which is the computer’s language. Interacting with computers becomes much more intuitive for people when computers can understand human language.

Natural language processing presents a variety of difficulties, most of which stem from the fact that natural language is always changing and always ambiguous.

The thought of machines comprehending human language seemed unthinkable not long ago. However, NLP has emerged as one of the most promising and fastest-growing topics within AI in a very short period, fuelled by research and breakthroughs in linguistics, computer science, and machine learning.

Natural language processing is critical to the advancement of technology and how people interact with it. Chatbots, cybersecurity, search engines, and big data analytics are just a few examples of real-world uses in both the commercial and consumer worlds. NLP is likely to continue to be a key element of the industry and everyday life, despite its problems.