What is Natural Language Processing?

Natural Language Processing (NLP) is present in our daily lives. Natural language processing is present in:-

• Voice recognition in smartphones
• Spam filters in email inboxes
• Customer support chatbots
• Translation of the pages in foreign languages
• Report generation in our analytical tools

Natural language processing is a set of techniques through which computers and people can interact.

NLP refers to the language used by humans to communicate with each other. This communication can be verbal or textual.

Natural language processing is a field that brings together computer science, artificial intelligence, and linguistics.

NLP is an incredibly important thing for computers to understand for a few reasons:-

• It can be viewed as a source of a huge amount of data and can capitulate useful information
• It can be used to allow computers to better communicate with humans
• Natural language processing describes a branch of artificial intelligence that automates language recognition and generation so that computers and humans can communicate seamlessly.

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

• Semantics – relations between words, sentences, and paragraphs (word meaning)
• Syntax – structural governance of the texts (grammar)
• Pragmatics – the way context contributes to meaning (conversation)

Natural Language Processing deals with different aspects of language such as:-

• Morphology – structure, and content of word forms (tense)
• Phonology – a systematic organization of sounds in the language

Computers can’t easily understand natural languages. Few sophisticated techniques and methods are required to translate natural language into a format understandable by computers.

NLP is the application area that helps to achieve this objective. It’s widely being used for machine learning, information summarization, human-computer interaction, etc.

NLP is a class of technology that seeks to process, interpret, and produce natural languages like English, Hindi, Chinese, Spanish, Russian, etc.

Natural language processing is often engaged with artificial intelligence techniques designed to automate the learning process.

NLP is the means of accomplishing a particular task. It’s a combination of computational linguistics and artificial intelligence.

It uses the tool of artificial intelligence such as algorithms, data structures, formal models for representing knowledge, model or reasoning processes, etc.

Natural language processing is being processed through two ways as:-

First – parsing technique

Second – transition network

In Natural language processing, the computer is required to have knowledge of basic alphabet, lexicon, grammar, and words formation to interact with the database in natural languages.

The inputs are in the form of natural language given by the user after the parsing process the output in the language is being understood by the application program.

Natural language processing is for the purpose of achieving human-like language processing for a range of tasks or applications.

NLP is a computerized approach to analyzing text that is based on a set of theories and a set of technologies.

Naturally arisen texts can be of any language, mode, genre, and these texts can be oral or written. The only requirement is that it should be in human language to communicate with one another.

Some good use of Natural Language Processing includes:-

• Extracting data from complicated data sources
• Answering queries phrased in natural language
• Detecting phishing
• Making secure financial transactions
• Handling insurance transactions

Origin of NLP Technology –

The first practical application of natural language processing was the translation of the messages from Russian to English to understand during the cold war.

The results were uninspired, but it was the first step in the right directions. Early natural language processing software was inflexible and not very practical and the major issue was flexibility.

It took decades before the computers become powerful enough to handle natural language processing operations.

Because language is complex and there is much more behind the words that were beyond the algorithm’s reach. The algorithm had required a lot of supervision and close attention to the details.

Natural language processing models become more of a routine job with the emergence of big data and machine learning.

How Natural Language Processing Work –

First, you need to understand a language’s set of rules, grammar, and then understand that there is no one set for all languages.

Each natural language processing systems work little differently but the process is always similar.

The system breaks down each word into its part of speech. It’s achieved through a series of grammar rules driven by algorithms to establish meaning and context.

The algorithm key is a semantic analysis which reduces sentences to their basic structure and looks for patterns to establish context.

It’s the analytical capability that enables the computer to understand that words can have different meanings to a particular usage of the word.

Natural language processing early approaches involved a highly rules-based approach.

The machine learning algorithms were trained to look for specific words and phrases in the text and to give specific responses when those phrases appeared.

The algorithms were trained using a large amount of data to sharpen their ability and improve accuracy.

Natural languages processing new generation models are based on deep learning technology that can access free text, and identify and retrieve relevant information.

Deep learning also looks for trends and patterns within unstructured data using neural networks to improve a computer’s understanding.

Natural Language Processing approaches for understanding semantic analysis are:-

• Distributional – it uses large-scale statistical tactics of Machine Learning and Deep Learning
• Frame-Based – the sentences which are syntactically different but the semantically same are represented inside data structure (frame) for the stereotyped situation
• Theoretical – this approach is based on the idea that sentences refer to the real world and parts of the sentence can be combined to represent the whole meaning
• Interactive Learning – it involves a pragmatic approach and the user is responsible for teaching the computer to learn the language step by step in an interactive learning environment

Process of Natural Language Processing –

When the text is composed of speech, and speech-to-text conversion is performed. The mechanism of natural language processing involves two processes:

Natural Language Understanding, and Natural Language Generation.

# Natural Language Understanding (NLU) – It can understand the meaning of the given text. Natural Language Understanding will resolve following ambiguity present in natural language for understanding structure:-

• Lexical Ambiguity – words have multiple meanings
• Syntactic Ambiguity – sentence having multiple parse tree
• Semantic Ambiguity – sentence having multiple meanings
• Anaphoric Ambiguity – previously used phrase or word has a different meaning

The meaning of each word can be understood through lexicons (vocabulary) and the set of grammatical rules.

Natural language understanding involves:-

• Processing the text (structuring a piece of unstructured data)
• Analyzing its content to extract insights of relevance (names mentioned in the article or figures related to market growth)
• Subsequently preparing it for some utilization (generate custom responses)

# Natural Language Generation (NLG) – It’s the process of automatically producing text from structured data in a readable format with meaningful phrases and sentences.

Natural Language Generation is a subset of natural language processing. NLG is divided into three stages:-

• Text Planning – the basic content ordering in structured data
• Sentence Planning – the combination of sentences from structured data to represent the flow of information
• Realization- grammatically correct sentences are produced to represent text

Application Areas of Natural Language Processing –

Natural Language Processing is currently being used in a variety of areas to solve difficult problems.

After the Google Knowledge Graph and Hummingbird were released, Google started reading human language through natural language processing.

Some of the researched tasks in natural language processing that have real-world applications are as follow:-

# Enterprise Search – Companies often has access to records of natural language that contain valuable information.

Natural Language Processing allows to quickly and easily search for relevant information within a huge amount of documents.

Enterprise search allows the user to search for information by asking a question in the same way as they ask to another human being.

The computer can understand the question’s context and return with the results related to the actual query.

Product reviews or tweets on Twitter can contain specific complaints or requests related to a product/service which can help prioritize and evaluate proposals.

Any information which is representational of natural language can also be useful for building powerful applications like bots response to the question or any software that translates from one language to another.

# Sentiment Analysis – Sentiment analysis is widely used in the web and social media monitoring. It allows businesses to get wide public opinion on the organization and its services.

The natural language processing techniques are very useful for sentiment analysis. Sentiment analysis applies to understand the opinions and concerns of the people producing the content.

It’s commonly used to uncover insights in data from social media. Companies use sentiment analysis to understand about them and their products or services.

NLP helps to identify the sentiment among several online posts and comments. The business makes use of NLP techniques to know about the customers’ opinion about their product and services from the online reviews.

# Automatic Text Summarization – The Automatic text summarization can be performed more efficiently using natural language processing.

Automatic summarization is relevant to summarize the meaning of document and information. Natural Language Processing enables you to produce summaries of text documents.

It’ll find out within the document to get relevant information and can automatically create a summary of the original document.

But it also understands the emotional meanings of the information in collecting data from social media.

Automatic text summarization is relevant when used to provide an overview of a news item or blog posts. It avoids redundancy from multiple sources and maximizing the diversity of content obtained.

# Email Filters – It’s one of the common use cases of natural language processing.

Spam classification is a great example of the use of NLP which refers to the process of classifying emails as spam based on the contents of the email.

NLP tools and techniques help convert the text of these emails into feature transmitter that can be used by machine learning applications to train the algorithm and then predict a new email as spam.

It analyzes the text in the emails that flow through the servers, email providers can stop spam based email contents from entering their mailbox.

# Machine Learning – It involves automatic translation of one human language into another. Google and other search engines base their machine learning technology on deep-learning NLP models.

A large scale data is being created on every day with the help of natural human languages such as free text in social posts and emails.

Natural language processing is used to perform tasks such as emotion detection, sentiment analysis, dialogue act recognition, spam email classification, etc.

Machine learning techniques require data to train algorithms. Natural language in the form of tweets, blogs, websites, chats, etc. is a huge source of data.

NLP plays a very critical role in data collection by converting natural language into a format that can be used by machine learning techniques to train algorithms.

# Speech Recognition – Human-computer interaction has evolved from simple mouse and keyboard desktop-based interaction to more natural interaction involving speech and gestures.

Natural language processing based tools are enabling companies to create intelligent voice-driven interfaces for any system.

When a person speaks in his/her language, the machine will be able to recognize the speech and convert it to its corresponding textual representation.

Businesses are employing NLP technologies to understand human language and queries. The company’s platform depends on a custom knowledge graph which is created for each application.

It performs a much better job identifying concepts which are relevant in the customer domain.

Amazon’s Alexa and Apple’s Siri are examples of such interaction. Google’s homepage can also perform search operations via speech.

# Information/Text Extraction – It uses NLP to extract structured information from unstructured data. Natural language processing can extract useful information from the document that can be used for different purposes.

NLP can define the context and extract the relevant part of the information from any digital document.

Public trading companies are required to publish financial information and make it available to their shareholders.

NLP can be used to extract financial information from these kinds of documents to automatically gather information on how a company is doing.

Many of the business decisions in industries like finance are driven sentiments influenced by the news. The majority of the news content is in the form of text, infographics, and images.

NLP can take the text, analyze, and extract the related information in a format that can be used in decision-making capabilities.

The programs with advanced statistical algorithms are capable of using statistical inference to understand the human conversation by calculating the probability of certain results.

Conclusion – Information is the main currency of the modern world. The insights and understanding of the context are the valuable elements of information. The semantics is the key to understanding the meaning and extracting the valuable insight out of available data. Natural Language Processing algorithm is an important aspect of interpreting data.