Natural Language Processing is a branch of Artificial Intelligence that enables the machines to read, understand and interpret the human language. Its main focus lies in the interaction between human language and Data Science.

Most of the techniques used in NLP depend on Machine Learning and Deep Learning to extract value from human language.

How NLP Works?

The first step in NLP depends on the type of application being used. In the case of voice-based systems, the first step involves the translation of words into text mainly using Hidden Markov Models (HMM). HMM involves usage of math models to understand what you said and translate into text which is then processed by the NLP system.

The next step involves understanding the context and the language by breaking every part of the sentence into its part of speech. A series of coded grammar rules that depend on algorithms are used for this step. These algorithms use statistical ML to help the NLP system understand the context of the word.

In the case of other scenarios where speech-to-text is not involved, the NLP system skips the first step and moves directly into interpreting words using grammar rules and algorithms.

NLP uses two main techniques for understanding human language; Syntax and Semantic analysis.

Syntax involves the arrangement of words to make sense grammatically. Syntax analysis enables NLP to derive meaning from a language based on grammatical rules.

Some of the syntax techniques include the following;

• Parsing – Analyzing a sentence for grammar
• Sentence breaking – Placing sentence boundaries for large texts
• Word Segmentation – Dividing a large piece of text into smaller units
• Morphological Segmentation – Dividing words into groups
• Stemming – Dividing words with inflection to its root forms

The semantic analysis involves the extraction of exact meaning from the text. It helps the NLP system to understand the meaning and structure of sentences and to interpret human language logically.

NLP uses the following semantic techniques to understand sentences:

• Sense Disambiguation – Deriving the meaning of a word using its context
• Named Entity Recognition – Helps to identify the words that can be categorized into groups
• Natural Language Generation – Usage of a database to extract semantics behind words

Common Uses of NLP

Chatbots

NLP can help improve the chatbots by training them for a particular behavior before deploying them. Chatbots use NLP algorithms for answering customer queries. These algorithms help the chatbots to understand a customer query and answer to those queries automatically in real-time.

Sentiment Analysis

Sentiment Analysis is a common application of NLP that can determine the positive or the negative polarity of a text. It can be used to classify reviews of a company or its products or poll customer’s opinion based on their social media posts and comments. This helps to provide customer insights on products or services.

NLP cannot single-handedly perform this task, it requires integration with ML and DL to perform back-end computation and data analytics to understand the data on a large scale.

Email Assistant

Grammar and spell check, auto-correct and auto-complete are some of the everyday use cases of NLP. Email filtering that keeps the spam mails away also uses NLP to determine the type of emails to keep in your inbox and sort out the spam mails.

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