All You Need To Know About Natural Language Processing and Its Implementations

What is NATURAL LANGUAGE PROCESSING?

Natural language processing is a divaricate of artificial intelligence that works as a bridge to connect computers with the human language. It particularly examines a large amount of natural language data to program the computers for further processing and analysis of that data.

Natural language processing (NLP) is a tie-up between the computer and the human language. It analyzes the speech of a language in both audible as well as text form. The system of NLP comprehends, elucidates, and encloses a specific meaning of human language by inputting words from different sentences, paragraphs, pages, etc.

It includes knowledge from different disciplines like computer science and computational linguistics. Humans may have any language as their native language, but a computer’s native language is named a machine language unfathomable to many people. A computer does not communicate through words; instead, it uses millions of zeroes and ones.

How does NLP work?

There are more than 6,500 languages all over the world, and each one of them has its own syntax. Human language is very complicated and divergent; it was a tricky task to make a computer understand each language in an organized way. Natural Language Processing helps computers to renovate data into an explicit and understandable form. It is known as data pre-processing. Data pre-processing is a procedure to modify a text in such a form that it is easy for a computer to understand, like in the form of machine language.

Several techniques can be used in NTP. Some of them are:

• Tokenization

The technique of breaking down the text into smaller semantic units is known as tokenization.

• Part-of-speech-tagging

The technique of labeling words as nouns, verbs, etc.

• Stemming

The technique of standardizing words by limiting them to their root forms.

Common Implementations of Natural Language Processing

Natural Language Processing lets you carry out multiple functions. Some of them are:

∙ Text Classification

∙ Text analytics

∙ Text Extraction

∙ Data analysis

∙ Machine Translation

∙ Digital phone calls

∙ Topic Modeling

∙ Natural Language Generation (NLG)

∙ Language translation

∙ Email filters

∙ Predictive text

∙ Smart assistants

∙ Search results

1. Text Classification

Text classification is one of the most salient tasks of NLP. It consists of different tags given to the text based on its content. Text classification is made through various methods like sentiment analysis (labeling a text as positive, negative, or neutral), topic classification (recognition of the central theme), and intent detection (identifying the purpose or objective of the text).

2. Text Analytics

The process of customization of unstructured word data into structured data for analysis through different linguistic, analytical, statistical, and machine learning tactics is named text analytics.

3. Text Extraction

Text extraction consists of the withdrawal of specific chunks of data already present in the text. It is a simple way of summarizing the text and searching keywords. The process is used for keyword extraction (pulling out essential words from the text) and for NER (known as Named Entity Recognition which means extracting the names of people or places).

4. Data Analysis

Implementing a language to analyze the data is known as data analysis. It elevates accessibility but lessens the obstruction to analytics across organizations beyond the anticipated bunch of analysts and software developers.

5. Machine Translation

It includes online tools like Google Translate, speech recognition, speech-to-text converter, etc. It utilizes a variety of natural language processing approaches to bring up the accuracy of human levels while translating speech and text into various languages.

6. Digital Phone Calls

NLP system applies to digital phone calls like a client’s calls to a service’s frontman, huge telecommunications providers or online talk bots, etc.

7. Topic Modeling

In this application of natural language processing, pertinent topics are searched in a text by assembling the text with identical words and expressions.

8. Natural Language Generation (NLG)

It is a work of natural language processing that embodies the analysis of unstructured data and avails it as an input to automatically generate content, answers, emails, and even books.

9. Language Translation

Translating a specific language into another is a messy task to do. Natural language processing allows online translators to translate languages more accurately with correct grammar and syntax.

10. Email Filters

It is one of the inaugural applications of NLP. It began with spam filters that point out a spam message. Now, after an update of Gmail, the system acknowledges the emails into three categories; primary, social, and promotions depending upon their contents.

11. Predictive Text

It includes things like autocorrect, autocomplete, and predictive text, commonly in our smartphones. It is most related to search engines and predicts stuff based on your type.

12. Smart Assistants

The application of recognizing voice and speech patterns is included in intelligent assistants like Alexa by Amazon. These assistants deduce the meaning of a thing by themselves and put up a beneficial comeback.

13. Search Results

Search engines use natural language processing to raise admissible results based on similar search behaviors. Google not only predicts popular searches applying according to your questions but also recognizes what you are trying to search. You can get a flight status only by putting the flight’s number. Natural language processing links your vague questions to the exact results.

14. Analyzing Customer Feedback

Natural language processing automatically analyzes the responses of their clients and what their customers think about their company and its products. Natural language processing provides qualitative data and insights that help you improve your work. This data can be in the form of online surveys, product reviews, social media promotional posts, etc.

Best Natural Language Processing Tools

The Natural Language Processing tools make language processing accessible to everyone. The best natural language processing tools are:

• MonkeyLearn
• GenSim
• Aylien
• SpaCy
• IBM Watson
• TextBlob
• Google Cloud NLP
• Stanford Core NLP
• Amazon Comprehend
• NLTK
• Lexalytics
• Clarabridge
• MeaningCloud