The present and future of natural language processing
Natural Language Processing (NLP) is an
artificial intelligence field that enables machines to interpret, understand
and infer meaning from human languages. It is a discipline that focuses on the
interaction of data science and human language and scales across many
industries. Today, NLP is booming due to the enormous changes in data access
and the rise in computational power, which helps practitioners to produce
meaningful results in fields such as education, media, banking, and human
resources.
NLP is thriving within the healthcare industry. New
technology improves patient delivery, diagnosis of disease, and reduces costs
as healthcare facilities are rapidly embracing electronic health records. The
fact that it is possible to improve clinical documentation implies that
patients can be better understood and benefit from better healthcare. The aim
should be to improve their experience, and this is already being achieved by
several organizations.
What is natural language processing?
Natural Language Processing (NLP) is a sub-field of
computer science, linguistics, information engineering, and artificial
intelligence. All these technologies have known to be related to computer and
human (natural) language interactions, especially how computers are programmed
to process and analyze large amounts of natural language data.
Governments' ability to use these innovations in
language processing defies restriction. Massive numbers of unstructured
documents are held by governments. For the kind of deep-pattern analysis
traditionally reserved for computational databases, fragmented information
presented in fundamentally non-mathematical formats is now accessible at
volumes far too large for human evaluation. NLP can open up new insights, more
tailor-made services and faster responses to information to the public sector.
Some use cases that show the power of NLP in the present era
·
Natural language
processing in healthcare
Medical services that are used to extract the
conditions of the disease can handle meditation sessions and monitor treatment
outcomes using clinical trial reports, electronic health records, and patient
notes. This is an example of NLP in health analysis where it is possible to use
NLP to predict various diseases utilizing pattern recognition methods and
speech of patients and their electronic health record.
·
Sentiment
analysis using natural language processing
Companies and organizations are now focused on
various ways to get to know their customers in order to provide personalized
interaction. The emotions behind the words can be calculated by using sentiment
analysis (which can only be achieved using NLP). The feeling analysis has the
ability to offer a lot of knowledge about the actions of the consumer and their
decisions that can be taken into account as critical decisive factors.
·
Cognitive
Analytics and natural language processing
This is the best example of different technologies
working together, but both fall under Artificial Intelligence's same roof. The
conversational systems that can take commands through the voice medium or the
text medium are feasible using natural language processing. Using cognitive
analytics, this generation of a technical ticket related to a technical issue
is now possible to automate various technical processes and handle it in an
automated or semi-automated manner. The application of these technologies can
lead to an automated process of managing technical problems within an
organization or can also provide the customer with an automated solution to
certain technical problems
Trends that drive the natural language processing industry
1. Unsupervised and supervised learning
It is a common fact that machine learning provides
significant support for natural language with applications of both supervised
and unsupervised learning, particularly in text analytics. Once the word in a
document is understood by Natural Language Processing and its parts of speech,
unsupervised learning may establish mathematical relationships. Supervised
learning is then based on the product of the relationship determinations of unsupervised
learning.
2. Reinforcement learning
By reinforcement learning, a variety of natural
language generation (NLG) tasks, such as text description, are being explored.
Although reinforcement learning approaches show potential outcomes, they
require appropriate action and state-space management, which may restrict the
model's significant power and learning skills.
3. Deep learning
The support of natural language through Deep
Learning is as significant as it is multifaceted. Techniques such as Recurrent
Neural Networks will leverage to provide a very accurate classification for the
use of parsing results and thus gain common grip in certain text analytics
platforms for classification of documents and labeling of entities.
4. Semantic search
Another development expected to significantly
revolutionize natural language and machine learning in the coming year is the
need for semantic search. The search involves both the interpretation of
natural language and the comprehension of natural language and necessitates a
precise understanding of the central ideas in the text. Organizations that want
to search through their document collection need the intelligence that comes
from Natural Language Processing and Machine Learning into a search-based
framework. This not only helps to inject back into operations but also to
develop smart search or semantic search applications.
5. Cognitive communication
Text analytics is anticipated to remain the most
extensive natural language use case in the years to come. Nonetheless, in use
cases involving speech-to-text, smart chatbots, and semantic search, these
technologies will also become more popular. Instigated by deep learning
frameworks, unsupervised and supervised machine learning, the proliferation of
natural language technologies will continue to influence cognitive computing's
communication ability.
The big picture
The natural language
processing market is
evolving with a wide range of growth factors such as increased adoption of
smart devices and the increase in technological investments in the healthcare
environment. Developments in the processing of natural language have data
governance connotations. It collects enormous amounts of user data, posing critical
legal issues regarding data ownership, privacy, and protection.
Big tech companies like Google, Facebook, Microsoft,
Amazon, and others will take over more control of what we see and do, but they
are not the government and for governments to be successful, new regulations on
how data is collected and disseminated via NLP need to be developed,
specifically where Natural Language Processing is linked to financial gain.
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