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Natural Language Processing Use Cases for Business Optimization

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Natural Language Processing Use Cases for Business Optimization

  1. 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                            NLP Use Cases for Business Optimization ​0 
  2. 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.  NLP Use Cases for Business Optimization ​1 
  3. 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:  NLP Use Cases for Business Optimization ​2 
  4. 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.                      NLP Use Cases for Business Optimization ​3 
  5. 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.  NLP Use Cases for Business Optimization ​4 
  6. 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.               NLP Use Cases for Business Optimization ​5 
  7. 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 NLP Use Cases for Business Optimization ​6 
  8. 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:  NLP Use Cases for Business Optimization ​7 
  9. 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.  NLP Use Cases for Business Optimization ​8 
  10. 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.  NLP Use Cases for Business Optimization ​9 
  11. 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.  NLP Use Cases for Business Optimization ​10 
  12. 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.  NLP Use Cases for Business Optimization ​11 

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