Explore how NLP empowers AI in conversational interfaces, sentiment analysis, language translation, text summarization, and information extraction. Discover the future of AI.
NLP is used successfully today in speech pattern recognition, weather forecasting, healthcare applications, and classifying handwritten documents. There are in fact so many NLP applications in business we ourselves use daily that we don’t even realise how ubiquitous the technology really is.
Natural language processing possesses an ability to let computer understand human speech and language, is a trendsetter in web development for coming years.
A Guide to Natural Language Processing NLP.pdfSoluLab1231
Natural Language Processing (NLP) is a subfield of AI that focuses on the interaction between computers and human languages. It aims to enable machines to understand, interpret, and generate human-like text or speech.
NLP has been used in a variety of applications, including:
Machine translation
Information retrieval
Sentiment analysis
Chatbots
In recent years, NLP has witnessed remarkable advancements, driven by the availability of large datasets of text and speech, the development of new machine learning algorithms, and the increasing computational power of computers. These advancements have made it possible for NLP to be used in a wider range of applications, and to achieve higher levels of accuracy.
For starters, it is difficult to understand the language of AI, thus it is important to know it’s terminologies as well as basic functions to understand the technology.
2025 Tech Events To Discuss The Revolutionizing Interactions With Advances In...Internet 2Conf
This presentation explores how Natural Language Processing (NLP) is changing the way we talk to machines and each other. It covers the latest breakthroughs and how they help businesses understand people better. The exciting part? These topics will be up for discussion at tech events in USA in 2025, like the Internet 2.0 Conference. Learn about the tools and techniques that are making conversations with computers more natural and intuitive than ever before.
Explore how NLP empowers AI in conversational interfaces, sentiment analysis, language translation, text summarization, and information extraction. Discover the future of AI.
NLP is used successfully today in speech pattern recognition, weather forecasting, healthcare applications, and classifying handwritten documents. There are in fact so many NLP applications in business we ourselves use daily that we don’t even realise how ubiquitous the technology really is.
Natural language processing possesses an ability to let computer understand human speech and language, is a trendsetter in web development for coming years.
A Guide to Natural Language Processing NLP.pdfSoluLab1231
Natural Language Processing (NLP) is a subfield of AI that focuses on the interaction between computers and human languages. It aims to enable machines to understand, interpret, and generate human-like text or speech.
NLP has been used in a variety of applications, including:
Machine translation
Information retrieval
Sentiment analysis
Chatbots
In recent years, NLP has witnessed remarkable advancements, driven by the availability of large datasets of text and speech, the development of new machine learning algorithms, and the increasing computational power of computers. These advancements have made it possible for NLP to be used in a wider range of applications, and to achieve higher levels of accuracy.
For starters, it is difficult to understand the language of AI, thus it is important to know it’s terminologies as well as basic functions to understand the technology.
2025 Tech Events To Discuss The Revolutionizing Interactions With Advances In...Internet 2Conf
This presentation explores how Natural Language Processing (NLP) is changing the way we talk to machines and each other. It covers the latest breakthroughs and how they help businesses understand people better. The exciting part? These topics will be up for discussion at tech events in USA in 2025, like the Internet 2.0 Conference. Learn about the tools and techniques that are making conversations with computers more natural and intuitive than ever before.
Demystifying Natural Language Processing: A Beginner’s Guidecyberprosocial
In today’s digital age, the realm of technology constantly pushes boundaries, paving the way for revolutionary advancements. Among these breakthroughs, one particularly fascinating field gaining momentum is Natural Language Processing (NLP). It refers to the ability of computers to understand, interpret, and generate human language in a way that is both meaningful and contextually relevant. This article aims to shed light on the intricacies of NLP, its applications, and its significance in various sectors.
What is NLP and Why NLP is important.pdfAiblogtech
Natural Language Processing (NLP) is an intriguing topic of artificial intelligence (AI) that focuses on the interplay of computers and human languages. It is the technology that allows computers to read, interpret, and synthesize meaningful and usable human language. This article will define NLP and explain why it is so crucial in today's digital environment.
Understanding Natural Language Processing (NLP)
NLP entails the creation of algorithms and models that enable robots to grasp and interact with human language. This means that computers can read and comprehend text, conduct jobs such as translation and sentiment analysis, and even converse with humans. NLP is concerned with not just understanding the words themselves, but also the context, nuances, and emotions expressed by language.
The Importance of NLP
NLP has become increasingly important in the digital age, with applications in a variety of areas. Here are some of the reasons why NLP is significant:
1. Communication with Machines
NLP helps to bridge the gap between humans and machines. It enables us to engage with computers and devices using normal language, making technology more accessible to those who are not technically sophisticated. NLP is used by voice assistants such as Siri and Alexa to understand and respond to our spoken requests.
2. Text Analysis
Every day, massive amounts of text data are generated in today's data-driven society. This content may be analyzed and important insights extracted using NLP technologies, which is critical for corporations, researchers, and organizations. Sentiment analysis, for example, assists businesses in understanding how their customers feel about their products or services.
3. Machine Translation
NLP is important in breaking down linguistic barriers. NLP techniques are used by translation services such as Google Translate to produce accurate translations between multiple languages. This is crucial for global communication and cross-cultural understanding.
4. Content Recommendation
Have you ever noticed how streaming services like Netflix and music applications like Spotify recommend material to you? NLP algorithms examine your viewing or listening habits and utilize that information to recommend films, songs, or shows that you are likely to appreciate.
5. Customer Support
Many companies are incorporating chatbots into their websites and customer service systems. These chatbots employ NLP to interact with clients, answer queries, and provide service around the clock. This not only improves customer service but also lowers business operating costs.
6. Healthcare
NLP is used in medicine to extract relevant information from patient data and medical literature. It can aid in disease diagnosis, patient monitoring, and even outbreak prediction, hence enhancing healthcare services and patient outcomes.
7. Social Media Monitoring
Social media interactions are monitored using NLP techniques. Individuals and businesses can monitor their internet
Chatbots are becoming increasingly popular in the business world as a means of automating customer service and support. Natural Language Processing (NLP) is the backbone of chatbots, enabling them to understand and respond to human language. In this blog post, we will explore various NLP techniques that developers can use to enhance the functionality of chatbots.
Chatbots are becoming increasingly popular in the business world as a means of automating customer service and support. Natural Language Processing (NLP) is the backbone of chatbots, enabling them to understand and respond to human language. In this blog post, we will explore various NLP techniques that developers can use to enhance the functionality of chatbots.
Natural Language Processing (NLP) is a branch of artificial intelligence (AI) that focuses on the interaction between computers and human language. It encompasses a range of techniques and technologies that enable machines to understand, interpret, and generate human language in a way that is meaningful and useful.
https://hiretopwriters.com/
Accenture's report explains how natural language processing and machine learning makes extracting valuable insights from unstructured data fast. Read more. https://www.accenture.com/us-en/insights/digital/unlocking-value-unstructured-data
Chatbot Service Providers | Chatbot Solution Providers | Ai Chatbot PlatformsElfo Digital Solutions
Elfo offers you 24/7 chat robot service platform. You can deploy your existing web application through our Chatbot Builder and get more leads and sales. We offer a free 14-day trial hosting on our platform to ensure the configuration, installation, and proper operation of your chatbot. To know more just visit this link: https://www.elfo.com/bot
The Rise of the AI Concierge: How Digital Assistants Will Impact Customer Exp...Kevin Getch
The Rise of the AI Concierge: How Digital Assistants Will Impact Customer Experience and Marketing Strategies
The digital marketing landscape is constantly evolving, but the rise of AI technology is ushering in a wave of unprecedented change. Businesses that fail to adapt risk missing out on a tremendous opportunity. In this presentation, we'll:
- Explore how AI-powered digital assistants, are transforming customer experience and marketing strategies.
- Uncover the four key macro trends shaping the future of marketing.
- Share concrete examples of how businesses can optimize for AI assistants and leverage them in their strategies to gain a competitive edge.
Use of natural language processing in semantic search.pdfTutors India
Semantic search may provide further business benefits by merging NLP with an intuitive user interface and making it straightforward for anyone to interact with and get the results they’re looking for.
For More Info (Source):
https://www.tutorsindia.com/blog/semantic-search-using-natural-language-processing/
𝐓𝐚𝐤𝐞 𝐚 𝐭𝐨𝐮𝐫: 𝐎𝐮𝐫 𝐥𝐚𝐭𝐞𝐬𝐭 𝐁𝐥𝐨𝐠 𝐢𝐬 𝐏𝐮𝐛𝐥𝐢𝐬𝐡𝐞𝐝 𝐧𝐨𝐰👉 The Powerful Landscape of Natural Language Processing.
Click: https://bit.ly/2UUeftt
NLP has changed the way we interact with machine and computers. 𝐖𝐡𝐚𝐭 𝐬𝐭𝐚𝐫𝐭𝐞𝐝 𝐚𝐬 𝐜𝐨𝐦𝐩𝐥𝐢𝐜𝐚𝐭𝐞𝐝, 𝐡𝐚𝐧𝐝𝐰𝐫𝐢𝐭𝐭𝐞𝐧 𝐟𝐨𝐫𝐦𝐮𝐥𝐚𝐬 is now a streamlined set of algorithms powered by AI.
𝐍𝐋𝐏 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 will be the underlying force for transformation from data driven to intelligence driven endeavors, as they shape and improve communication technology in the years to come.
Croud Presents: How to Build a Data-driven SEO Strategy Using NLPDaniel Liddle
Exploring how you can harness the huge amounts of data available to build an effective, empirically-led SEO strategy using machine learning resource such as natural language processing (NLP). Including useful and practical tips on areas such as topic modelling, categorisation and clustering, so you can get started on using NLP in your own SEO strategy right away.
As more and more organizations move from recognizing that unstructured data exists, and remains untapped, the field of semantic technology and text analysis capabilities is
SEO + NLP - Redefining The Computer & Human Relationship.pdfLet's Get Visible
NLP is constantly used by software & computers. For SEO, understanding how this affects your keywords and phrases is vital for success.
NLP (Natural Language Processing) is the process of computer science that allows computers the ability to understand the text and spoken words as closely to the human language as possible.
NLP is constantly used by software and computers – think Siri, Alexa, translation software, voice assistance, social media monitoring tools, chatbots, spam filters, and more importantly search engines.
China Real Estate - In Desperate Need of More Policy MeasuresSG Analytics
China’s real estate sector has been significantly impacted by the introduction of the three red lines policy, eventually leading to a liquidity crunch in the sector. The impact of the initial policy measures is considered to be short-lived, as the housing market downturn and developer risks could have been worse than what was expected earlier. However, more concrete measures will be needed for a substantial recovery in the sector. https://www.sganalytics.com/whitepapers/china-real-estate-in-desperate-need-of-more-policy-measures/
Role of Data Analytics in the Media and Entertainment Industry - White PaperSG Analytics
As technologies evolve, there will be one universal
marketplace for content in the next decade. As a result,
there will be abundant content from across the world,
which will be accessible to customers across countries
and languages. The trend of purchasing global rights
will also continue. For instance, Indian shows have
begun infusing global casts to make their stories reach
everywhere. In 2023 and beyond, gaming is also set to play a critical role across the media and entertainment sector, as it will highlight communities in expanding entertainment licenses.
Download Whitepaper - https://us.sganalytics.com/whitepapers/role-of-analytics-in-making-better-content-decisions/
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Demystifying Natural Language Processing: A Beginner’s Guidecyberprosocial
In today’s digital age, the realm of technology constantly pushes boundaries, paving the way for revolutionary advancements. Among these breakthroughs, one particularly fascinating field gaining momentum is Natural Language Processing (NLP). It refers to the ability of computers to understand, interpret, and generate human language in a way that is both meaningful and contextually relevant. This article aims to shed light on the intricacies of NLP, its applications, and its significance in various sectors.
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Understanding Natural Language Processing (NLP)
NLP entails the creation of algorithms and models that enable robots to grasp and interact with human language. This means that computers can read and comprehend text, conduct jobs such as translation and sentiment analysis, and even converse with humans. NLP is concerned with not just understanding the words themselves, but also the context, nuances, and emotions expressed by language.
The Importance of NLP
NLP has become increasingly important in the digital age, with applications in a variety of areas. Here are some of the reasons why NLP is significant:
1. Communication with Machines
NLP helps to bridge the gap between humans and machines. It enables us to engage with computers and devices using normal language, making technology more accessible to those who are not technically sophisticated. NLP is used by voice assistants such as Siri and Alexa to understand and respond to our spoken requests.
2. Text Analysis
Every day, massive amounts of text data are generated in today's data-driven society. This content may be analyzed and important insights extracted using NLP technologies, which is critical for corporations, researchers, and organizations. Sentiment analysis, for example, assists businesses in understanding how their customers feel about their products or services.
3. Machine Translation
NLP is important in breaking down linguistic barriers. NLP techniques are used by translation services such as Google Translate to produce accurate translations between multiple languages. This is crucial for global communication and cross-cultural understanding.
4. Content Recommendation
Have you ever noticed how streaming services like Netflix and music applications like Spotify recommend material to you? NLP algorithms examine your viewing or listening habits and utilize that information to recommend films, songs, or shows that you are likely to appreciate.
5. Customer Support
Many companies are incorporating chatbots into their websites and customer service systems. These chatbots employ NLP to interact with clients, answer queries, and provide service around the clock. This not only improves customer service but also lowers business operating costs.
6. Healthcare
NLP is used in medicine to extract relevant information from patient data and medical literature. It can aid in disease diagnosis, patient monitoring, and even outbreak prediction, hence enhancing healthcare services and patient outcomes.
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Chatbots are becoming increasingly popular in the business world as a means of automating customer service and support. Natural Language Processing (NLP) is the backbone of chatbots, enabling them to understand and respond to human language. In this blog post, we will explore various NLP techniques that developers can use to enhance the functionality of chatbots.
Chatbots are becoming increasingly popular in the business world as a means of automating customer service and support. Natural Language Processing (NLP) is the backbone of chatbots, enabling them to understand and respond to human language. In this blog post, we will explore various NLP techniques that developers can use to enhance the functionality of chatbots.
Natural Language Processing (NLP) is a branch of artificial intelligence (AI) that focuses on the interaction between computers and human language. It encompasses a range of techniques and technologies that enable machines to understand, interpret, and generate human language in a way that is meaningful and useful.
https://hiretopwriters.com/
Accenture's report explains how natural language processing and machine learning makes extracting valuable insights from unstructured data fast. Read more. https://www.accenture.com/us-en/insights/digital/unlocking-value-unstructured-data
Chatbot Service Providers | Chatbot Solution Providers | Ai Chatbot PlatformsElfo Digital Solutions
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The digital marketing landscape is constantly evolving, but the rise of AI technology is ushering in a wave of unprecedented change. Businesses that fail to adapt risk missing out on a tremendous opportunity. In this presentation, we'll:
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- Uncover the four key macro trends shaping the future of marketing.
- Share concrete examples of how businesses can optimize for AI assistants and leverage them in their strategies to gain a competitive edge.
Use of natural language processing in semantic search.pdfTutors India
Semantic search may provide further business benefits by merging NLP with an intuitive user interface and making it straightforward for anyone to interact with and get the results they’re looking for.
For More Info (Source):
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𝐓𝐚𝐤𝐞 𝐚 𝐭𝐨𝐮𝐫: 𝐎𝐮𝐫 𝐥𝐚𝐭𝐞𝐬𝐭 𝐁𝐥𝐨𝐠 𝐢𝐬 𝐏𝐮𝐛𝐥𝐢𝐬𝐡𝐞𝐝 𝐧𝐨𝐰👉 The Powerful Landscape of Natural Language Processing.
Click: https://bit.ly/2UUeftt
NLP has changed the way we interact with machine and computers. 𝐖𝐡𝐚𝐭 𝐬𝐭𝐚𝐫𝐭𝐞𝐝 𝐚𝐬 𝐜𝐨𝐦𝐩𝐥𝐢𝐜𝐚𝐭𝐞𝐝, 𝐡𝐚𝐧𝐝𝐰𝐫𝐢𝐭𝐭𝐞𝐧 𝐟𝐨𝐫𝐦𝐮𝐥𝐚𝐬 is now a streamlined set of algorithms powered by AI.
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NLP (Natural Language Processing) is the process of computer science that allows computers the ability to understand the text and spoken words as closely to the human language as possible.
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China’s real estate sector has been significantly impacted by the introduction of the three red lines policy, eventually leading to a liquidity crunch in the sector. The impact of the initial policy measures is considered to be short-lived, as the housing market downturn and developer risks could have been worse than what was expected earlier. However, more concrete measures will be needed for a substantial recovery in the sector. https://www.sganalytics.com/whitepapers/china-real-estate-in-desperate-need-of-more-policy-measures/
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As technologies evolve, there will be one universal
marketplace for content in the next decade. As a result,
there will be abundant content from across the world,
which will be accessible to customers across countries
and languages. The trend of purchasing global rights
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everywhere. In 2023 and beyond, gaming is also set to play a critical role across the media and entertainment sector, as it will highlight communities in expanding entertainment licenses.
Download Whitepaper - https://us.sganalytics.com/whitepapers/role-of-analytics-in-making-better-content-decisions/
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1. Develop a comprehensive understanding of the fundamental principles and concepts that form the foundation of sustainability within corporate environments.
2. Explore the sustainability implementation model, focusing on effective measures and reporting strategies to track and communicate sustainability efforts.
3. Identify and define best practices and critical success factors essential for achieving sustainability goals within organizations.
CONTENTS
1. Introduction and Key Concepts of Sustainability
2. Principles and Practices of Sustainability
3. Measures and Reporting in Sustainability
4. Sustainability Implementation & Best Practices
To download the complete presentation, visit: https://www.oeconsulting.com.sg/training-presentations
The NLP disruption: top trends and use cases - SG Analytics
1. The NLP Disruption: Top
Trends and Use Cases
The NLP market is forecasted to be 14X larger in 2025 than it was in
2017, which means the value of NLP will rise from 3 bn dollars in 2017
to more than 43 bn dollars in 2025 (Statista).
Know more about how the NLP disruption is changing business
operations and the top trends in the field of NLP in this article.
Evolution of NLP
Commonly identified as NLP, Natural Language Processing
translates human commands into computer language, thereby
making the user interface much more convenient and easy to use.
NLP has evolved a lot since its origin in the 1940s. Initially termed as
Machine Translation (MT), NLP was invented to help soldiers
understand messages in foreign languages. This is when the idea
2. to convert human language into computer coding evolved, as in
those times computer language wasn’t something that could be
easily learned and only a handful of people could operate such
devices.
In current times, NPL is combined with the enabling technology,
Artificial Intelligence, and the combination has become extremely
popular within a short time. ALEXA is a perfect example of modern-
day NLP. Today, Alexa is used to set reminders, make calls and
send messages automatically, and even control TV, lights, and
other smart equipment through voice command.
A quick introduction to NLP
Natural Language Processing helps in the natural manipulation of
normal languages like text and speech and perfecting the
communication between humans and computers. In simple
terms, NLP enables computers to hear speech or read a text,
interpret it, identify the sentiment, and select the vital portions.
Following are the steps included in Natural Language Processing:
Parsing
Arguably the foundation of NLP, this process comprises the
following components- tokenization, tagging, and
limitization/stemming. Tokenization is the process of splitting
words or text. Tagging is the process of assigning different parts of
speech to different words. Limitization is something similar to
taxonomies, where the word will be mapped back to its root form
in order to identify variations across terms and spot spelling errors.
Tokenization
This process includes the extraction of characters, letters, or
syllables from single words or words within the same sentence,
that are not always straightforward. For example, it makes sense
to separate sentences between periods, but splitting dates or
abbreviations does not fit this practice. Tokens are basically
3. individual terms or sentences and tokenization refers to the
splitting of content, in terms of words or sentences.
4. Entity Extraction
Also known as name identity recognition or concept extraction,
entity extraction, is the process of assigning tokens to entities like
name, thing, place, etc. For instance, NLP models, with the help of
entities, can understand whether the word “CARY” refers to a
person or a town in North Carolina.
Classifications
Also referred to as categorizations, classification is the process of
labelling documents as per the befitting type. For instance,
documents relevant to regulatory compliance will be labelled so
for easy classification. Also, classification predominantly
compliments sentiment analysis and enables users to classify
comments that are positive, negative, and neutral.
NLP – How businesses can benefit from
it?
Right from discussion analysis and client support to reviewing and
requesting systems, NLP offers various business benefits. Also, NLP
facilitates higher consumer loyalty, improved analysis, reduced
costs, and more effective discussions when the right chatbot is
used for communication. Find below a couple of instances to
understand why chatbot needs natural language processing, and
how the two can aid businesses:
5. Improved market research and analysis
NLP-based chatbots and software systems can help in performing
in-depth market research and analysis by garnering information
from client reviews and media comments to internal and external
inquiry queries. With these chatbots, businesses can not only
gather data in real-time but also reduce expenditure on data
collection.
Mitigate inefficiency and cost
When the business is profitable, there are more possibilities to
mitigate costs whenever required. Every company aspires to
increase revenue. With the help of NLP-trained chatbots,
businesses can effectively reduce expenses associated with
duplicative and physical duties. This in turn also helps in improving
efficiency.
Evolving trends in NLP
Every technology is evolving. So is Natural Language Processing.
Following are some of the latest trends in NLP to look out for in 2021:
6. Market Intelligence Monitoring
NLP is anticipated to revolutionize the marketing field in the near
future, although at present it is used only in financial marketing.
With NLP, businesses can better determine market conditions and
other related information. In the coming years, there are increased
chances of NLP becoming a primary tool for monitoring market
conditions. Businesses are more likely to leverage intelligence
information from natural language processing to plan future
strategies.
Supervised and Unsupervised collaboration
NLP can reach the next level of potential when supervised and
unsupervised learning is used together. For instance, in text
analytics, people had to examine documents with utmost care.
Combining supervised learning with unsupervised learning helps
in making this task easier and less time-consuming. While one
helps in understanding all the terms associated with the topic, the
other helps in establishing the relationship between the terms.
Reinforcement learning to train NLP models
Reinforced Learning (RL) has improved significantly over the years.
Yet, even today, training of RL models is uncertain as well as a
time-consuming task. There are fields such as sample efficiency,
training time, and overall practices, in which Reinforced Learning
can be improvised. This generally occurs when a Reinforced
Learning model is trained from scratch. To solve this problem, data
experts have come up with a new technique – use supervised
models trained on NLP alongside RL to improve them.
Customized product recommendations
7. Consumers, particularly millennials, are enjoying shopping at the
comfort of their couches rather than hitting a brick and mortar
store. Therefore, retailers who plan to establish or expand their
online business can study changing consumer behaviours with the
help of NLP and the process of recommendation system. Some of
the famous shopping apps and websites like Amazon are
analyzing customer behaviours using customers’ buying history,
browsing history, wish list, etc. This information enables retailers to
provide customized product recommendations, thereby making it
easier for customers to browse and find products as well as
improve customer experience and satisfaction.
Simplify document classification
Deep learning has various applications in the field of NLP. This also
means that NLP might have certain limitations in terms of
accuracy. With RNN-Recurrent Neural Networks text classification
can be made easy and simple. Besides document classification,
RNN will bring about improvements in the area of the analytics
platform.
Sentiment Analysis for social media
Social media is of paramount importance for businesses today, as
they play a major role now in helping companies connect with
their customers and influencing their purchase decisions. NLP
helps in performing sentiment analysis more effectively. With the
help of sentiment analysis, companies can assess the nature,
mood, and views of the people on any particular topic like travel,
health, politics, etc.
Increased use of virtual assistants and chatbots
Virtual assistants and chatbots have become inevitable in today’s
AI-first world. The chatbot market that was estimated to be worth
nearly $2.6 bn in 2019 is expected to reach almost $9.4 bn by 2024
(analytics steps). Increased voice search means an increased
need for NLP.
8.
9. Intelligent semantic search
The brand-new semantic search technology can improve
searching using NLP. In other words, the browser will no longer
search for the individual meaning of the words within the entered
term rather will search for the broad meaning of the entered term.
Thus, users can find any type of information in a much easier and
quicker way.
Intelligent cognitive-communication
In a way, intelligent cognitive-communication is similar to
semantic search. In simple terms, cognitive-communication aims
to make communication easy and understandable for both
parties. Intelligent cognitive-communication will make use of
supervised and unsupervised learning along with other NL
technologies to improve the interpretability of any kind of
communication.
Wrapping Up
As consumer behaviour monitoring and business intelligence
solutions are becoming increasingly important, the relevance of
NLP will soar in the upcoming years. With natural language
processing, businesses can not only comprehend and analyze
customer responses but also assess the attitude and emotional
state of the customer more granularly and optimize their business
initiatives accordingly.