What is Natural Language Processing (NLP)?

What Is NLP?

Natural Language Processing (NLP) refers to the subfield of Artificial Intelligence (AI) that enables computers to process human language in order to analyze, understand, generate and/or automate the use of large amounts of unstructured human communication.

When processed by a NLP system, information from human language can be extracted, such as identifying words in text, identifying the relationships between terms, classifying text, analyzing the sentiment of written text, extracting entities from unstructured information, and more.

Modern NLP relies on a broad variety of methods from computer science, linguistics, AI and machine learning in order to process huge amounts of language data and discover patterns within it.

How Does NLP Work?

A typical NLP workflow goes through several stages that enable it to extract relevant information from input language, or even to generate language itself.

Collecting and Processing Text

For an NLP system, the process typically starts off with a collection of languages to work with, for example documents, websites, emails, customer conversations, social media posts etc.

These steps include cleaning up unwanted information in the text, like greetings or signatures, and then splitting up the text into the smallest units of language, such as individual words or even sentences, known as tokens.

Understanding Language

After processing the input, NLP models analyze the structure of the input languages. This can include the identification of words and their grammatical structure, and the entities in text and their interrelations.

For example, identifying the names of people, companies, places, products, and dates are all tasks that NLP can complete as a system.

Analyzing Meaning and Context

While recognizing individual words within a piece of text is an important step in NLP, it is not sufficient. In most cases, words within a text are interrelated and their meaning depends on the relationships between them.

Today’s NLP models use context in a wide range of applications, including determining the meaning of phrases, user intent, text classification, and sentiment (positive, negative, neutral).

Generating or Responding to Language

AI systems can also generate language, i.e. after processing input they can produce output in the form of text, for example, answers, abstracts, translations etc.

This capability is demonstrated in use in many applications including conversational AI, virtual assistants, automated writing tools and question answering systems.

What Are the Main Techniques Used in NLP?

There are a number of different techniques which are used when implementing NLP. These are usually determined by the task being completed, and the information that the program requires to extract.

Tokenization

Tokenization: Text is split up into individual tokens. Tokens can be words, parts of words or even single characters. The process of splitting text into individual tokens (words, characters, parts of words etc.) which the model can then process.

Part-of-Speech Tagging

Part-of-speech tagging: This tagger assigns to each word in a sentence a part-of-speech (like noun, verb, adjective, adverb etc.). The system then uses the tag given by the part-of-speech tagger to a word to determine its use in a sentence.

This tagger assigns to each word in a sentence a part-of-speech (such as noun, verb, adjective, adverb etc.). The system will use the tag given to the word by the part-of-speech tagger to work out how the word is being used in the sentence.

Named Entity Recognition

Named Entity Recognition (NER) – This technique identifies the specific entities in text, e.g. people, organizations, locations, dates, products, etc.

For example, MEII.AI is the organization name and Bangalore is the location name in the sentence “MEII.AI operates in Bangalore.”

Sentiment Analysis

Sentiment analysis (SA) is used to measure emotions in text and classify them in categories like positive, negative or neutral. The majority of applications use a simple three category classification, while more advanced models are able to recognize a large number of feelings and even opinions. 

Text Classification

Text classification refers to text that is classified into predefined categories, for example in customer service all incoming customer messages could be classified as billing questions, technical support issues, product suggestions or sales requests.

These texts can then be categorized into different groups to be managed by the organization that has set up the system.

What Are the Benefits of NLP?

NLP allows organizations and software applications to deal with large quantities of human language in an efficient way. It can automatically extract information from unstructured text, classify documents, carry out Sentiment Analysis and much more.

Information extraction from unstructured text, text classification, customer feedback analysis, improved search and automated responses to customer inquiries.

The main advantage of Natural Language Processing is that it makes software usage much more natural. You do not have to remember special commands and their sequences.

This can also enable many different applications in businesses today including document management and search, business analytics, and even language translation.

What Are the Limitations and Challenges of NLP?

The understanding of human communication is still a complex task for NLP systems. Language often depends on context, culture, tone and many more factors.

Understanding Context and Ambiguity

Understanding Context and Ambiguity – The same words can have very different meanings depending on the surrounding words and even sentences. Very often, NLP Systems struggle to understand statements that are not clear or where crucial information is missing.

Handling Different Languages and Dialects

Each language has its own set of grammatical rules, vocabulary, writing system, and even cultural nuances. The availability of training data can vary greatly between languages and also within a language for different dialects.

Bias in Training Data

As NLP models learn from their training data, every bias contained in the data is also reproduced in the model’s outputs and predictions.

Data Quality and Availability

NLP relies on the data that is used for training and testing the system. Low quality, insufficient, inconsistent or even outdated data may yield suboptimal results.

Understanding Sarcasm and Figurative Language

Sarcasm, humor, idioms, metaphors and other forms of indirect language, including implicit assertions are typically the hardest types of natural language to process accurately using NLP, because their meaning is not identical to their literal, ‘surface’ form.

Privacy and Security Concerns

Language data can be extremely sensitive in nature. This could include customers’ personal details, sensitive customer conversations, an organization’s internal documents and communications as well as highly sensitive and confidential business information.

What Are the Applications of NLP?

Chatbots and Virtual Assistants

Chatbots and Virtual Assistants: NLP enables chatbots and virtual assistants to understand user input and to respond back in natural language. Intent analysis is used to determine the information required to service the user inquiry.

Search and Information Retrieval

NLP is also used to improve search so that it is able to look for more than just keywords. It identifies entities, intent, relationships and more which enables improved retrieval of relevant information.

Sentiment Analysis

Sentiment Analysis – Organizations use NLP to extract customer feedback from various text-based sources. This feedback can help organizations to identify opinions and track recurring positive and negative feedback.

Text Summarization

Automatic summary generation of long documents, reports, articles, etc. (keeping the important parts).

Language Translation

Modern Machine translation systems, such as Google Translate, utilize NLP to translate text in one language to equivalent text in another language. In addition to basic translation, many modern translation systems also utilize context and words surrounding a word to create a much more natural translation.

Speech and Voice Applications

NLP can also be used to work with systems which perform speech recognition, allowing them to generate spoken responses to natural language input.

NLP vs. Natural Language Understanding (NLU)

The NLU aspect is often considered as a subcomponent of the broader NLP space and therefore is often used as part of a larger NLP solution.