Natural Language Understanding

- Overview

Natural language processing (NLP) is the ability of "intelligent" computer systems to understand human language (written and spoken). This is often called natural language. With the increasing volume of text data generated every day, from social media posts to research articles, NLP has become an essential tool for extracting valuable insights and automating various tasks.

NLP is a subfield of artificial intelligence (AI). It helps machines process and understand human language so that they can automate repetitive tasks. Examples include machine translation, summarization, ticket classification, and spell checking.

NLP has been around for more than fifty years, with the technology originating in linguistics, or the study of human language. It has various practical applications in many industries and fields, including intelligent search engines, advanced medical research, and business processing intelligence. 

Take sentiment analysis, for example, which uses NLP to detect emotions in text. This classification task is one of the most popular tasks in NLP and is often used by companies to automatically detect brand sentiment on social media. Analyzing these interactions can help brands identify urgent customer issues that require an immediate response, or monitor overall customer satisfaction.

NLP is not only concerned with processing, as recent developments in the field such as the introduction of Large Language Models (LLMs) and GPT3, are also aimed at language generation as well.

Although research in NLP covers a wide variety of tasks, most of it can be summarized into three themes: syntax, semantics, and relations.

- Natural Language Understanding (NLU)

Natural language processing (NLP) helps computers communicate with humans in their own language, making it possible for computers to read text, hear speech, interpret it, measure sentiment and determine which parts are important.  

Natural Language Understanding (NLU) or Natural Language Interpretation (NLI) is a subtopic of NLP in AI that deals with machine reading comprehension.

Natural language understanding (NLU) is considered an AI-hard problem. This field has attracted considerable commercial interest due to its applications in automated reasoning, machine translation, question answering, news gathering, text classification, speech activation, archiving, and large-scale content analysis.

NLU enables human-computer interaction by analyzing language rather than individual words. It uses syntactic and semantic analysis to determine the meaning of sentences. Syntax refers to the grammatical structure of a sentence, while semantics refers to its intended meaning.

The most common example of NLU is speech recognition technology. Speech recognition software analyzes spoken language and converts it into text or other data that computers can process. It is an important part of virtual assistants, allowing them to understand and respond to voice commands.

NLU uses syntactic and semantic analysis to enable computers to read and interpret language. For example, a virtual assistant might use NLU to understand a user's request to book a flight and then generate a response that includes flight options and pricing information. 

NLU has the following stages:  

• Tokenization: Split the given input into words or tokens
• Lexical analysis: Put the token into a dictionary containing its part of speech 

[More to come ...]