Phases, Techniques and challenges of NLP

- Overview

Natural language processing (NLP) is a branch of artificial intelligence (AI) that allows computers to understand spoken words and text in a similar way to humans. 

The five phases of NLP are: Lexical or morphological analysis, Syntax analysis, Semantic analysis, Discourse integration, Pragmatic analysis. 

Some NLP techniques include: 

• Semantic analysis: Analyzes the grammatical structure of sentences to determine the relationships between independent terms.
• Syntax analysis: Identifies the relationships between words and phrases within a sentence, and standardizes their structure.
• Sentiment analysis: Determines whether data is positive, negative, or neutral.
• Tokenization: Breaks characters, words, or subwords down into "tokens" that can be analyzed by a program.

 Other NLP techniques include: 

• Speech recognition
• Word modeling
• Vocabulary building
• Frequent word occurrence

- Evolution of NLP

Natural language processing (NLP) has evolved significantly since its inception in the 1940s. This field started when people realized the importance of automatically translating languages.

Early NLP systems were based on rule-based approaches. Linguists manually define grammatical rules and language structures. The first attempts to enable computers to understand and produce human language were made in the 1950s.

In the late 1980s, machine learning algorithms for language processing revolutionized NLP. Deep learning and Transformers enable models to handle the complexity and variability of natural language more effectively. Word embeddings also play a crucial role in enabling the model to capture subtle relationships between words.

Advances in NLP have led to the development of more sophisticated conversational AI systems and chatbots. They are deployed in customer service, virtual assistant and personalized support systems.

- Challenges of NLP

Although natural language processing faces many challenges, the benefits that NLP brings to businesses are substantial, making NLP a worthwhile investment. 

However, it is important to understand what these challenges are before starting NLP.

Human language is complex, vague, disorganized and diverse. There are more than 6,500 languages ​​in the world, each with its own syntactic and semantic rules.

Even humans have difficulty understanding language. Therefore, for machines to understand natural language, they first need to convert it into a language they can interpret. 

In NLP, syntax and semantic analysis are key to understanding the grammatical structure of text and identifying how words relate to each other in a given context. However, converting text into something a machine can process is complicated. 

Data scientists need to teach NLP tools to go beyond definitions and word order to understand context, single-word ambiguity, and other complex concepts associated with human language.

[More to come ...]