Natural Language Use Cases for Biz

1. Table of Contents
What Is Natural Language Processing and How Does It Work?
NLP Use Cases You Should Know About
1. NLP-Powered Epidemiological Investigation
2. Security Authentication With NLP
3. NLP-Based Brand Awareness and Market Research
4. Chatbots for Customer Support and Engagement
5. NLP-Powered Competitive Analysis
6. Report Auto-Generation With the Help of NLP
7. Real-Time Intelligence Gathering on Specific Financial Stocks
8. Defense Departments And Secret Services Using AI
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2. AI-powered human-to-machine interactions are nothing new. Public
organizations and businesses have been applying data science and machine
learning technologies for a while. One of the quickest evolving AI technologies
today is natural language processing (NLP).
A 2019 Statista ​report​ reveals that the NLP market will increase to 43.9 billion
dollars by 2025.
*Revenues from the natural language processing (NLP) market worldwide from 2017
to 2025 (in million U.S. dollars)
Clearly, many companies believe in its potential and are already investing into it.
But why is NLP becoming so popular year-over-year? And what might that mean
for businesses? These types of questions are fairly common.
In this article, we’re going to take a deep dive into NLP, its use cases, and other
relevant information that you may find useful.
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3. What Is Natural Language Processing and How Does It
Work?
In simple terms, natural language processing is AI technology that recognizes
and understands natural human languages. Written or spoken human speech is
converted into a form that computers are able to understand through NLP
techniques.
Most of us use NLP business applications every day without even knowing it.
Spell-checkers, online search, translators, voice assistants—almost all of these
include natural language processing technology.
There is a wide variety of NLP techniques known as “NLP tasks.” Here is a brief
breakdown of various NLP tasks performed by modern NLP software.
According to the experience of MobiDev data scientists, the following NLP
capabilities are particularly interesting due to the potential they have:
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4. Named Entity Recognition
Named entity recognition is the task that implies identification entities in a
sentence (like a person, organization, date, location, time, etc.), and their
classification into categories.
Example:
Part-of-Speech Tagging
Part-of-speech tagging is the task that involves marking up words in a sentence
as nouns, verbs, adjectives, adverbs, and other descriptors.
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5.
Example​:
Summarization
Summarization is the task that includes text shortening by identifying the
important parts and creating a summary. There are two approaches to text
summarization:
● Extractive Summarization.​ Identification of the important sentences or
phrases from the original text and extracting them from the text.
Example:
● Abstractive Summarization.​ New sentences generated from the original
text, where the generated sentences may not be present in the original text.
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6. Example:
Sentiment Analysis
Sentiment analysis is the task that implies a broad range of subjective analysis to
identify positive or negative feelings in a sentence, the sentiment of a customer
review, judging mood via written text or voice analysis, and other similar tasks.
Example:
Text Classification
Text classification is the task that involves assigning tags/categories to text
according to the content. Text classifiers can be used to structure, organize, and
categorize any text.
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7. Example:
Language Modeling
Language modeling is the NLP task that includes predicting the next
word/character in a text/document. Language models might be used for:
➢ Optical Character Recognition
➢ Machine Translation
➢ Image Captioning
➢ Text Summarization
➢ Handwriting Recognition
➢ Spelling Correction
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8. NLP Use Cases You Should Know About
1. NLP-Powered Epidemiological Investigation
When the Coronavirus outbreak hit China, Alibaba’s DAMO Academy developed
the ​StructBERT​ NLP model. Being deployed in Alibaba’s ecosystem, the model
powered not only the search engine on Alibaba’s retail platforms but also
anonymous healthcare data analysis. By analyzing the text of medical records
and epidemiological investigation, the Centers for Disease Control (CDCs) used
StructBERT for fighting against COVID-19 in China cities.
Being based on the BERT pre-trained model, StructBert not only understands the
context of words in search queries but also leverages the structural information:
sentence-level ordering and word-level ordering.
2. Security Authentication With NLP
With the arrival of NLP technology, it’s possible to integrate more advanced
security techniques. By using question generation, data scienеntists are able to
build stronger security systems.
How Does This Algorithm Work?
1. Find additional context for a user's personal information.
2. Extract import information (answers) using a named entity recognition model.
3. Generate questions with the neural network.
5. Validate a user’s answer.
At MobiDev, we run projects based on the question generation technique. The
video below shows the core ideas behind our research:
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9.
3. NLP-Based Brand Awareness and Market Research
It’s difficult to develop actionable business strategies when you don’t know how
customers feel about your brand. By using sentiment analysis and getting the
most frequent context when your brand receives positive ​and negative
comments, you can increase your strengths and reduce weaknesses based on
viable market research.​ NLP-based software analyzes social media content,
including customer reviews/comments, and converts them into insightful data.
How Does This Algorithm Work?
1. Analyze an entire list of comments and classify them using a sentiment
analysis model.
2. Get the most frequent words and phrases from both positive and negative
comments.
3. Perform market research based on the data collected.
Based on this algorithm, it is possible to assign a value to the output
information. This value might be considered as a positive, negative, or neutral
emotion. Marketers can use this data to make more informed decisions in their
marketing strategies and campaigns.
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10. 4. Chatbots for Customer Support and Engagement
As technology grows, customer service automation is becoming more advanced.
NLP-powered chatbots are a prime example of automation technology due to
their ability to perform personalized conversations and partially replace
humans. The most common approach is to use NLP-based bots that start the
interaction and take care of basic scenarios, and only bring in a human operator
to handle more advanced situations.
5. NLP-Powered Competitive Analysis
Most founders will conduct competitor analysis and research when starting a
business. This task enables them to better understand their market,
competitors, customers, and other important details about their industry.
There are dozens of tools available to help entrepreneurs monitor their
competitors. NLP-powered engines like​ ​Zirra​ simplify the process for
automatically building a competitive landscape. When Zirra analyzes something,
it gathers a list of companies and ranks them from zero to one. This rank shows
how closely these companies are related to each other using a multimodal
semantic field.
The algorithms solutions like Zirra use create the list of companies by scanning
the Internet for articles and putting the data into an NLP module that closes out
semantic relationships between companies.
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11. 6. Report Auto-Generation With the Help of NLP
Documenting and reporting are among the most time-consuming tasks for
businesses. NLP techniques allow you to convert unstructured text information
into reports by applying speech-to-text dictation and formulated data entry.
Using NLP, it’s possible to design a deep learning model that identifies necessary
information from unstructured text data and combines it into specific reports.
Sophisticated solutions like this can identify and request missing data and allows
you to automate the process.
How Does This Algorithm Work?
0. Define a template for the report and all possible sources of information.
1. Go through all data sources and find potential fillers for blank fields. This step
is similar to the named entity recognition task, but it’s necessary to train the
model to find its own classes.
2. Deliver the report to a responsible person in a suggestion mode.
7. Real-Time Intelligence Gathering on Specific Financial Stocks
The stock market is sensitive to news and world events. Many companies are
looking for ways to complete complex stock market analyses by accessing
historical stock price data, news archives, company reports, and other relevant
data.
Popular solutions like IBM’s Watson partially provide similar services. And
beyond that, there are other interesting AI-based technologies already being
used for stock analysis.
How Does This Algorithm Work?
1. Improve understanding of a large amount of news and data found in reports
similar to how sentiment analysis works.
2. Classify news and connect it to certain companies trading their stocks.
3. Figure out dependencies, such as how the stock market reacts to certain
news events.
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12. 4. Run continuous real-time news and reports analysis.
5. Predict and notify when the stock market shifts based on recent news and
events.
A successful solution would require a substantial amount of data science
modeling using machine learning activities like NLP processing. And more
importantly, a significant amount of computing power to calculate it all.
Remember, as the business goal becomes more precise, the easier it is to solve
it with high accuracy and a reasonable budget.
8. Defense Departments And Secret Services Using AI
This past fall, the Department of Defense released a ​document​ called
“Recommendations on the Ethical Use of Artificial Intelligence by the
Department of Defense”
The US government is already investigating use cases for AI technology. The
Defense Innovation Board is working with companies like Google, Microsoft, and
Facebook. All of these efforts are designed to provide a better framework for
understanding and controlling AI for defense & security.
But we still don’t know how NLP, deep learning, or predictive analysis have been
used for defense and security by top governments. There’s really no reason to
guess, but we can safely say that it’s been used and that its usage is growing
rapidly.
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