The document discusses artificial intelligence for text analytics and natural language processing. It provides an introduction to text analytics and NLP, explaining that text analytics extracts useful information from text sources while NLP makes natural language accessible to machines. It then discusses how AI can enable applications like competitive intelligence, human resource management, and market analysis by automatically analyzing large amounts of text data. The document also provides an overview of how natural language processing works using deep learning techniques.

1. Artificial Intelligence
for text analytics and
Natural Language Processing
PRESENTED BY,
Nishmi Suresh
MTech
Embedded System

2. INTRODUCTION
• There has been a significant growth in the volume and variety of data
because of the accumulation of unstructured text data.
• Companies are now relying on technologies like text analytics and
Natural Language Processing (NLP) for making sense of such
massively collected data.
• Text analytics and NLP hold the key to unlocking the business value
within these huge data sets.
• NLP is concerned with making natural language accessible to
machines, while text analytics refers to the extraction of useful
information from text sources.

3. Cont..
• Today, text analytics and NLP are gradually transforming into a field
extremely useful for various business applications, such as competitive
analysis, and improving the quality of machine intelligence systems.
• A right platform and AI implementation can enable businesses to fully
utilize their data lake and take advantage of the latest text analytics
and NLP algorithms.

5. What is text analytics?
• Text analytics is about deriving high-quality structured
data from unstructured text.
• Another name for text analytics is text mining.
• A good reason for using text analytics might be to extract
additional data about customers from unstructured data
sources to enrich customer master data
• To produce new customer insight or to determine
sentiment about products and services

6. Cont..

7. Cont..
• Implementing artificial intelligence in text analytics can lead to
development of the following applications

8. Competitive Intelligence
• Some companies are required to organize and modify their strategies
according to market demands and opportunities that are available.
• Due to this, companies acquire, manage, and analyze an enormous
amount of data to keep themselves relevant in the market.
• The process of manually compiling documents according to a business
and customer’s needs and preferences into reports is very labor
intensive.
• Problem is further intensified when it needs to be updated frequently.
• With competitive Intelligence, users are able to select only relevant
information by the automatic reading of this data.
• Once the material has been collected, it is automatically classified into
the relevant categories, and subsequently analyzed to get answers for a
specific business problem or for formulating a crucial strategy.

9. Human Resource Management
• Text analytics techniques combined with AI are also used to manage
human resources, mainly applications that aim at analyzing staff’s
opinions, monitoring the level of employee satisfaction, as well as storing
and reading CVs for the selection of new personnel.
Market Analysis
• Text analytics with AI can also be used for analyzing competitors or
monitoring customers’ opinions, identifying new potential customers,
along with determining the companies’ image through the analysis of
press reviews and other relevant sources.
• Organizations today are increasingly looking to apply this technique to
derive useful operational and business insights for improving their market
prospects.

15. How natural language processing works ?
• Current approaches to NLP are based on deep learning, a type of AI that
examines and uses patterns in data to improve a program's understanding.
• Deep learning models require massive amounts of labelled data to train on
and identify relevant correlation
• Assembling this kind of big data set is one of the main hurdles to NLP
currently.
• Earlier approaches to NLP involved a more rules-based approach, where
simpler machine learning algorithms were told what words and phrases to
look for in text and given specific responses when those phrases appeared.
• But deep learning is a more flexible, intuitive approach in which algorithms
learn to identify speakers' intent from many examples, almost like how a
child would learn human language.

17. Cont….
• Question and Answering: find answers to natural language
questions in a text collection or database
• Summarization: generate a short biography from one or more
new articles
• Search: involves allowing users to query data sets in the form
of a question that they might pose to another person
• Customer Support: include machine translation, translate a
document from one human language to another