What is natural language processing (NLP)?
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Natural language processing has been around for over 50 years and has its roots in linguistics. During the mid-1900s, the first idea of machine translation was devised. Alan Turing's paper "Turing Test" and Noam Chomsky’s book “Syntactic Structures” revolutionized rule-based translation in the history of NLP. After that, successful NLP systems such as SHRDLU, ELIZA, and chatbots were developed. Many machine learning algorithms, along with statistical modeling, were introduced.
In the early 2000s, supervised and unsupervised learning came into the picture along with the considerable amount of data accessible for research purposes. Currently,
Natural language processing
Natural language processing (NLP) is a branch of Artificial Intelligence (AI) that makes human language understandable to machines.
NLP combines the power of linguistics and computer science to investigate the patterns and structure of language and develop intelligent systems capable of interpreting, analyzing, and extracting meaning from text and speech (based on machine learning and NLP algorithms).
Working
In general, four major steps are involved in NLP as illustrated:
These steps are listed below:
Lexical analysis is breaking down a sentence into words or small units known as tokens to determine the meaning and relationship of each token to the complete statement.
Syntactic analysis determines the relationship between different words and phrases inside a sentence, standardizes their structure, and expresses the relationships in a hierarchical framework.
Semantic analysis associates language-independent meanings to syntactic structures at the levels of phrases, clauses, sentences, and paragraphs.
Output transformation is generating an output based on semantic analysis of text or speech that meets the application's objective.
Databases are used to store, manage, and access data. The information gathered can be observed and analyzed. Databases can be thought of as an organized collection of information.
Trends in NLP
Statistical NLP has emerged as the primary method for modeling complex natural language tasks. However, with technological advancement, deep learning-based NLP has recently brought a paradigm shift.
Statistical NLP
Statistical Language Modeling, also known as Language Modeling (LM), is the creation of probabilistic models that can predict the next word in a sequence based on the terms that occurred before it.
A statistical language model learns the likelihood of word occurrence based on text samples. Simpler models may view the context of the brief sequence of words, but larger models may consider sentences or paragraphs. Language models typically work at the word level.
Applications
Applications of statistical models in NLP are as follows:
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Recognize handwriting via lexical acquisition, even in a text that is written poorly
Locate and correct spelling mistakes
Speech recognition
Identify fundamental acoustic features
Perform text classification
Deep learning NLP
Unlike statistical models in NLP, various deep learning models have been used to improve, accelerate, and automate text analytics functions and NLP features.
Furthermore, these models and methodologies provide improved solutions for converting unstructured text into useful data and insights. Deep learning models allow us to learn the meaning of words or phrases by analyzing their use in a paragraph.
Applications
Deep learning is used for the following NLP processes:
Tokenization and text classification
Generating captions for images
Speech recognition
Document summarization
Language Modeling
In recent years, the attention mechanism in deep learning has improved the performance of various models.
Attention models
Attention models or attention mechanisms are the deep learning approaches that emphasize a particular component. In deep learning, attention focuses on a specific attribute and identifies its significance.
The model often focuses on one component of the architecture that is in charge of maintaining and evaluating the interdependent interaction between input elements, known as self-attention, or between input and output elements, known as general attention.
Note: Want to read more about attention mechanisms? Click here.
Examples
NLP is currently gaining prominence due to the rising use of AI technologies. There are several real-world examples of NLP technology that impact our daily life.
Some of the popular applications are the ones listed below.
Smart assistants like Siri, Alexa, and Cortana are ingrained in our daily lives. They use NLP to break down language into parts of speech, word stems, and other linguistic features. The rest is handled by natural language understanding (NLU), which allows machines to understand language, and natural language generation (NLG), which gives devices the ability to "talk."
GPT-3, third generation Generative Pre-trained Transformer, is a neural network machine learning model trained to generate any type of text from internet data.
GPT-3 has been used to generate articles, poems, stories, news stories, and dialogue from a small amount of input text.
It is also used for automated conversational tasks, such as responding to the text entered into the computer with a new piece of text relevant to the context.
Unlike human language text, it can construct anything with a text structure. Additionally, it is capable of automatically producing programming code as well as document summaries.
Note: Curious about GPT-3? Click here
