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.

1. The Powerful
Landscape of Natural
Language Processing
www.polestarllp.com

2. We all grew up watching science-fiction movies where the
hero interacts with machines and robots on his mission to
get his ladylove held by the villains. Much like
Trigonometry in Mathematics, NLP for a greater part
remains to be a fairly independent and intriguing
component of data science. The combination of AI,
computer science, and computational linguistics has
made it possible through Natural Language Processing
(NLP). The technique incorporates machine-based
algorithms which can derive meaning from both verbal
and written communication.

3. What is NLP?
Natural language processing (NLP) is a branch of
artificial intelligence that helps computers understand,
interpret and manipulate human language. NLP draws
from many disciplines, including computer science and
computational linguistics, in its pursuit to fill the gap
between human communication and computer
understanding.

4. Natural Language Processing relies on
two basic concepts:
 Natural Language Understanding (NLU)
 Natural Language Generation (NLG)
Let's get into the roots of
Natural Language Processing.
Concepts

5. Natural Language Understanding (NLU) - relies on algorithms
to break down human speech into computable characteristics
or properties called feature vectors, with AI helping to refine
the recognition of things like timing, intent, and sentiment. In
this way, NLU can understand input via text or speech.
Natural Language Understanding

6. Natural Language Generation (NLG) - refers to the computer's
capability to generate text, whether by converting data to
written language, translating speech to written text, or
converting text to audible speech. Speech-to-text and Text-to-
speech engines depends on NLG to give coherent messages,
backed by a predefined lexicon and a set of grammar rules.
Most of the big names in technology have introduced NLP
applications, including – Amazon, Microsoft, Google, IBM, and
more.
Natural Language Generation

7. Approaches
Approaches in Natural Language Processing can be
divided into three categories-

8. Rule-based systems
depends heavily on crafting
domain-specific rules such
as - regular expressions. It
can be used to solve simple
problems like - extracting
structured data emails,
web-pages (unstructured
data), but due to the
complexity of human
natural languages, rule-
based systems fail to build
models.
Classical Machine Learning
approaches are used to solve
harder problems which rule-
based systems can't solve very
well like - Spam Detection. It
relies on a more general
approach to understanding
language, using hand-crafted
features (e.g. sentence length,
part of speech tags, the
occurrence of specific words)
then providing those features to
a statistical machine learning
model, which learns different
patterns in the training set and
then be able to reason about
unseen data.
Deep Learning models are
the newest part of NLP
research and applications
now; they generalize even
better than the classical
machine learning
approaches as they don't
need hand-crafted features
because they automatically
work as feature extractors,
which helped a lot in
building end-to-end models
(little human-interaction).

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11. Some amazing Natural
Language Processing Examples
Speech Recognition
According to eMarketer data, up to 111.8 million
people in the US – over a third of its population–will
use a voice assistant at least once a month. The
advances in NLP, speech recognition solutions are
getting smarter and delivering a better experience
for users. A stunning example of speech recognition
comes from the movie Star Trek (1996) where the
computer always seemed to be able to identify who
was speaking and distinguishing between voice
commands and conversations between crew
members.

12. Search Engines
Another typical example of NLP is web search
engines. NLP employs robust parsing, grammar
rules, and algorithms to derive intent from
people's utterances. So, with advances in the
field of natural language, machines can better
handle pragmatics, deciphering context to
understand the meaning behind a statement
better." As a result, NLP has become more
ubiquitous for both consumers and businesses.

13. Natural Language Processing
in Today's World
NLP has come a long way from Machine Translation, given the
vast array of technology available. Today, families across the
globe are welcoming virtual additions like Alexa into their
homes. According to a survey, 47.3 million U.S. adults now own
a smart speaker, a platform wholly dependent on NLP for
survival by intaking a user's commands and applying
algorithms to decipher the language and formulate responses.

14. Google recently announced expansions to
its Cloud AutoML platform, including NLP
and translation.
Chatbots are another implementation
of NLP on the rise. A great example is
Chaplin AI, powered by Polestar
Solutions is the one link which can
help you to leverage the entire
investment.
In a recent Oracle survey, 80% of
respondents said they already used or
planned to use chatbots by 2020 for
consumer-facing products.

15. Future of Natural Language Processing
The core aim of NLP is to dominate human-to-machine
interaction to the point where talking to a machine is as easy as
talking to a human. NLP will continue to harness unstructured
data and make it more meaningful to a machine. IDC recently
forecasted that "the amount of analyzed data 'touched' by
cognitive systems will grow by a factor of 100 to 1.4 ZB by 2025,"
impacting thousands of industries and companies around the
globe. Robotics, health care, financial services, connected auto
and smart homes are just a handful of the sectors that will
continue to be advanced by NLP.

16. 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.
Conclusion

17. Thank you!
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