Overview of Artificial Intelligence, Deep Learning and NLP in Big Data

Artificial Intelligence Overview

AI refers to ‘Artificial Intelligence’ which means making machines capable of performing quick tasks like human beings. AI performs automated tasks using intelligence.

The term Artificial Intelligence has two key components -

Goals & Applications of Artificial Intelligence

Evolution of Artificial Intelligence

It is a set of algorithms used by intelligent systems to learn from experience.

These are the advanced round of algorithms used by machines to learn from experience. E.g. - Deep Neural Networks.

Artifical Intelligence technology is currently at this stage.

It is self-learning from experience without the need for external data.

 

3 Types of Artificial Intelligence

It comprises of primary/role tasks such as those performed by chatbots, personal assistants like SIRI by Apple and Alexa by Amazon.

Artificial General Intelligence comprises of human-level tasks such as performed by self-driving cars by Uber, Autopilot by Tesla. It involves continual learning by the machines.

Artificial Super Intelligence refers to intelligence way smarter than humans.

What Makes System AI Enabled

Difference Between AI, NLP, ML, DL & Neural Networks

Building systems that can do intelligent things.

Building systems that can understand language. It is a subset of Artificial Intelligence.

Building systems that can learn from experience. It is also a subset of Artificial Intelligence.

A biologically inspired network of Artificial Neurons.

Building systems that use Deep Neural Network on a large set of data. It is a subset of Machine Learning.

What is Natural Language Processing(NLP)?

Natural Language Processing (NLP) is “ability of machines to understand and interpret human language the way it is written or spoken.”

The objective of NLP is to make computer/machines as intelligent as human beings in understanding language.

The ultimate goal of NLP is to the fill the gap how the people communicate (natural language) and what the computer understands (machine language).

There are three different levels of linguistic analysis done before performing NLP-

NLP deal with different aspects of language such as

Approaches of NLP for understanding semantic analysis

The real success of NLP lies in the fact that humans deceive into believing that they are talking to humans instead of computers.

Importance of Natural Language Processing(NLP)

With NLP, it is possible to perform certain tasks like Automated Speech and Automated Text Writing in less time.

Due to the presence of significant data (text) around, why not we use the computers untiring willingness and ability to run several algorithms to perform tasks in no time.

These tasks include other NLP applications like Automatic Summarization (to generate summary of given text) and Machine Translation (translation of one language into another)

Process of Natural Language Processing

In case the text is composed of speech, speech-to-text conversion is performed.

The mechanism of Natural Language Processing involves two processes -

Natural Language Understanding

NLU or Natural Language Understanding tries to understand the meaning of given text. The nature and structure of each word inside text must be known for NLU. For understanding structure, NLU attempting to resolve following ambiguity present in natural language -

Next, the sense of each word is understood by using lexicons (vocabulary) and set of grammatical rules.

However, certain different words are having similar meaning (synonyms) and words having more than one meaning (polysemy).

Natural Language Generation

It is the process of automatically producing text from structured data in a readable format with meaningful phrases and sentences. The problem of natural language generation is hard to deal. It is subset of NLP

Natural language generation divided into three proposed stages -

Difference Between NLP and Text Mining

Natural language processing is responsible for understanding meaning and structure of given text.

Text Mining or Text Analytics is a process of extracting hidden information inside text data through pattern recognition.

Natural language processing is used to understand the meaning (semantics) of given text data, while text mining is used to understand structure (syntax) of given text data.

As an example - I found my wallet near the bank. The task of NLP is to figure out in the end that ‘bank’ refers to financial institute or ‘river bank.'

What is Big Data?

According to the Author Dr. Kirk Borne, Principal Data Scientist, Big Data Definition is described as big data is everything, quantified, and tracked.

Big Data For Natural Language Processing

Today around 80 % of total data is available in the raw form. Big Data comes from information stored in big organizations as well as enterprises. Examples include information about employees, company purchase, sale records, business transactions, the previous record of organizations, social media, etc.

Though human uses language, which is ambiguous and unstructured to be interpreted by computers, yet with the help of NLP, this large unstructured data can be harnessed for evolving patterns inside data to know better the information contained in data.

NLP can solve significant problems of the business world by using Big Data. Be it any business of retail, healthcare, business, financial institutions.

What is a Chatbot?

Chatbots or Automated Intelligent Agents

Why Are Chatbots Essential For Business

Chatbots are critical to understanding changes in digital customer care services provided and in many routine queries that are most frequently enquired.

Chatbots are useful in a certain scenario when the client service requests are specified in the area and highly predictable, managing a high volume of similar requests, automated responses.

How Does A Chatbot Work?

 

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Chatbots learn each time they make interaction with the user trying to match the user queries with the information in the knowledge base using Machine Learning.

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Deep Learning For NLP

3 Capability Levels of Deep Learning Intelligence

There are of course other capabilities that also need to be considered in Deep Learning such as Interpretability, modularity, transferability, latency, adversarial stability, and security. But these are the main ones.

Applications of Deep Learning in NLP

Deep Learning Algorithms

NLP Usage

Neural Network - NN (feed)

 

  • Part-of-speech Tagging

  • Tokenization

  • Named Entity Recognition

  • Intent Extraction

Recurrent Neural Networks -(RNN)

 

  • Machine Translation

  • Question Answering System

  • Image Captioning

Recursive Neural Networks

 

  • Parsing sentences

  • Sentiment Analysis

  • Paraphrase detection

  • Relation Classification

  • Object detection

Convolutional Neural Network -(CNN)

 

  • Sentence/ Text classification

  • Relation extraction and classification

  • Spam detection

  • Categorization of search queries

  • Semantic relation extraction

 

Difference Between Classical NLP & Deep Learning NLP

 

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NLP For Log Analysis and Log Mining

What is Log?

A collection of messages from different network devices and hardware in time sequence represents a log. Logs may be directed to files present on hard disks or can be sent over the network as a stream of messages to log collector.

Logs provide the process to maintain and track the hardware performance, parameters tuning, emergency and recovery of systems and optimization of applications and infrastructure.

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What is Log Analysis?

Log analysis is the process of extracting information from logs considering the different syntax and semantics of messages in the log files and interpreting the context with application to have a comparative analysis of log files coming from various sources for Anomaly Detection and finding correlations.

What is Log Mining?

Log Mining or Log Knowledge Discovery is the process of extracting patterns and correlations in logs to reveal knowledge and predict Anomaly Detection if any inside log messages.

Role of NLP in Log Analysis & Log Mining

Natural Language processing techniques are widely used in Log Analysis and Log Mining.

The different techniques such as tokenization, stemming, lemmatization, parsing, etc. are used to convert log messages into structured form.

Once logs are available in the well-documented form, log analysis, and log mining is performed to extract useful information and knowledge is discovered from information.

The example in case of error log caused due to server failure.

Natural Language Processing Techniques

Different methods used for performing log analysis are described below

It is one such technique which involves comparing log messages with messages stored in pattern book to filter out messages.

Normalization of log messages is done to convert different messages into the same format. This is done when different log messages have different terminology, but the same interpretation is coming from various sources like applications or operating systems.

Classification & Tagging of different log messages involves ordering of messages and tagging them with the various keywords for later analysis.

It is a kind of technique using Machine Learning Algorithms to discard uninteresting log messages. It is also used to detect an Anomaly in the ordinary working of systems.

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Diving into Natural Language Processing

Natural language processing is a complex field and is the intersection of Artificial Intelligence, computational linguistics, and computer science.

Getting started with Natural Language Processing 

The user needs to import a file containing text written. Then the user should perform the following steps for natural language processing.

Technique

Example

Output

Sentence Segmentation

Mark met the president. He said:”Hi! What’s up -Alex?”

  • Sentence 1 - Mark met the president.

  • Sentence 2 - He said: ”Hi! What’s up - Alex?”

Tokenization

My phone tries to ‘charging’ from ‘discharging’ state.

  • [My] [phone] [tries] [to] [‘] [charging] [‘][from] [‘][discharging] [‘] [state][.]

Stemming/Lemmatization

Drinking, Drank, Drunk

  • Drink

Part-of-Speech tagging

If you build it he will come.

  • IN - prepositions and subordinating conjunctions.

  • PRP - Personal Pronoun

  • VBP - Verb Noun 3rd person singular present form.

  • PRP- Personal pronoun

  • MD - Modal Verbs

  • VB - Verb base form

Parsing

Mark and Joe went into a bar.

  • (S(NP(NP Mark) and (NP(Joe))

  • (VP(went (PP into (NP a bar))))

Named Entity Recognition

Let’s meet Alice at 6 am in India.

  • Let’s meet Alice at 6 am in India

  • Person Time Location

Coreference resolution

Mark went into the mall. He thought it was a shopping mall.

  • Mark went into the mall. He thought it was a shopping mall.

Key Application Areas of Natural Language Processing

Apart from use in Big Data, Log Mining, and Log Analysis, it has other significant application areas.

Although the term ‘NLP’ is not as popular as ‘big data’ ‘machine learning’ but we are using NLP every day.

Given the input text, the task is to write a summary of text discarding irrelevant points.

It is done on the given text to predict the subject of the text, eg, whether the text conveys judgment, opinion or reviews, etc

It is performed to categorize different journals, news stories according to their domain. Multi-document classification is also possible. A famous example of text classification is spam detection in emails.

Based on the style of the writing in the journal, its attribute can be used to detect its author name.

Information extraction is something which proposes email program add events to the calendar automatically.

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Text Analytics or Text Mining refers to the automatic extraction of high-value information from text. The extraction involves structuring the input text, discovering patterns in the structured data and interpreting the results. Text Mining process involves Machine Learning, Statistics, Data Mining, and Computational Linguistics. Sentiment Analysis Using Machine Learning, NLP, and Deep Learning

At Don, we process and analyze textual content and provide valuable insights by transforming the raw data into structured, usable information. Don's Text Analytics Solutions offers Part-of-Speech (PoS) tagging, Clustering, Classification, Information Extraction, Sentiment Analysis and more. 

Sentiment Analysis helps to apprehend people's reaction to situations. Sentiment Analysis is used to predict person's emotions like angry, happy, sad, disgust etc. 

Don offers Sentiment Analysis and Intent Analytics using Machine Learning, Natural Language Processing, Deep Learning, Supervised Learning Algorithms, Keras with Tensorflow. Enhance the customer experience through Sentiment Analysis in Business. 

Build, Deploy and Manage Intelligent Chatbots to interact naturally with a user on Website, Apps, Slack, Facebook Messenger and more. Don Chatbot Solutions uses Cognitive Intelligence that enables bot to see, hear, and interpret in more human ways.