Natural language processing is a great way to pivot the future of technology. It is the tools of Natural language processing techniques that make the world go round. The technological advancements across a diversified range of industries have proven the multi-faceted access and applicability of the advances in technology.
The Global Natural language processing market is projected to reach USD 239.9 billion by 2032; soaring at a CAGR of 31.3% (as researched by Allied Market Research). This is the clear sign of the expanse of the technology over the future period and it is time to understand what it takes to pivot the future with advancements in place.
Businesses worldwide have been making the growth trajectory for their niche with the timely deployment of specific natural language processing tools and NLP Algorithms that are strewn in to make the amplified growth possible. With the rise in research and development of NLP and AI tools; it has become essential to comprehend the current scenario of the Natural language processing applications and techniques; that can enable an advanced worldwide growth landscape.
What is Natural Language Processing?
NLP is a clever machine learning technology that allows computers the ability to interpret, manipulate, and comprehend human language. It enables easy translation and response to spoken commands and summarizes large volumes of text in practically no time.
Features of Natural Language Processing:
Tokenizing words, speech tagging, facial and voice recognition, and many others are just a reflection of what NLP can perform. Let us begin by understanding some of the core features of Natural Language Processing:
- Content categorization
- Large Language Models-based classification
- Corpus Analysis
- Contextual extraction
- Sentiment Analysis
- Speech-to-text and Text-to-speech Conversion
- Document Summarization
- Machine Translation
- Speech Tagging
- Named Entity recognition
- Topic Modelling
- Keyword Analysis
What makes Natural Language Processing important in today’s times?
- NLP algorithms and tools help in communicating with humans in their own language and scale other language-related tasks.
- Can analyze language-based data than humans; in a consistent unbiased manner.
- Text analysis and speech data analysis is easily automated
- Structures a highly unstructured data source
- Models human language with syntactic and semantic understanding and domain expertise
- Resolves ambiguity in language and adds useful numeric structure to the data for many downstream applications; such as speech recognition or text analytics.
A qualified machine learning or AI engineer specializes in utilizing quality Python codes and AI tools to bring out the most effective solution for the problem at hand. The time is now to make the right move into a career that promises a lasting thriving trajectory ahead. Investing your time and resources in mastering top NLP Tools and strategies is the way ahead as the worldwide tech landscape grows beyond measure.
10 Most Popular Natural Language Processing Tools and Platforms Perfect for 2024:
Stores textual data in the form of strings; provides for a variety of features such as tokenization, stemming, tagging, classification, and much more.
Offers only a selected set of tools that are best suited to save time and confusion for developers; as it works with text stored in the form of objects.
Popularly used for word embedding as words are converted to vectors based on their dictionary meaning, and they further train ML models.
- Amazon Comprehend
A software-as-a-service; it gives the user inference from the analysis of textual documents; simplifying the document processing job of the users.
A good choice for handling large data volumes as it is used for topic modeling, similar text recognition, document navigation, and much more.
- Core NLP
A Java-based open-source library; used for speech tagging, tokenization, named entity recognition, and decoding dates, times, and numbers.
- Google Cloud Natural Language
Builds on Google’s language understanding technology that enables ML models using AutoML features.
- Generative Pre-trained Transformer
Trained on a sizeable textual dataset that autofills documents, and generates content for websites or blogs, and beyond.
Utilizes Python Codes and Java and is stored locally or remotely for textual data processing.
Includes a lot of NLTK functionalities without the complexity and makes for a perfect choice for beginners.
It is worth every moment to understand these tools and enable oneself with the top-raiding Natural Language Processing technologies and tools that are churning out better career opportunities and yielding higher benefits for businesses worldwide. With the global AI Market Size expected to grow 37% every year from 2023 to 2030 as per Grand View Research statistics; it is believed that NLP is going to bring in the most waves among other nuanced technologies. Also, another score from PwC showcases that over 40% of business leaders reported an increase in productivity through AI automation and hiring qualified AI engineers with the best AI certifications among other core credentials. This is enough reason to start now! Beginning with the most promising AI tools and technology investment as far as your career projections go; the time is ripe. Natural Language Processing is the way we look into the future starting now.