Natural Language Processing

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Natural Language Processing (NLP) is the process of understanding human language. It is used in a variety of applications such as automatic translation, text summarization, and sentiment analysis.

There are various techniques used in natural language processing, such as statistical methods, rule-based systems, and machine learning.

The most advanced NLP implementation is based on Deep Learning, which is a type of machine learning that uses artificial neural networks.

One of the most popular applications of NLP is automatic translation. Automatic translation can be used to translate text from one language to another or to generate a machine translation of a spoken sentence.

Machine translation is used by businesses to translate documents and websites and by people, as well as in a number of industries, such as finance, healthcare, and manufacturing. For example, NLP can be used to identify the sentiment of customer reviews in order to improve customer service.

There are various types of NLP techniques, each with its own advantages and disadvantages. The most important factor when choosing a technique is the data set that is being used.

NLP is a very important tool for understanding and working with human language. It has a variety of applications in different industries, and it is constantly evolving to become better at understanding human language.


in Different Languages

Translate text from one language to another.

Natural Language Generation

Natural Language Generation: Text continuation.

Talk to me like Shakespeare do

Generate poetry in Shakespeare's stye.

Text Classification

Classify text according to the ad-hock provided classes

Sentiment Analysis

Is the feedback good or bad?

Named Entity Recognition

List of recognized entities within the text.
Resolved automatically.

Named Entity Recognition, Extended

List of recognized entities within the text.
Resolved automatically. More of them.