The Natural language toolkit (NLTK library) is a popular open source package of libraries used for all sorts of NLP tasks. In this article, we will be using the NLTK library for all the steps of Text Preprocessing.


Neural networks and machine translation Rogue Connect
Neural networks and machine translation

NLP conveyor

The implementation of any complex task usually means building a pipeline (pipeline).

The essence of this approach is to break the problem down into a number of sequential subtasks and solve each of them separately. In building a pipeline, two parts can be conditionally distinguished: preprocessing the input data (usually it takes the most time) and building the model. There are seven main stages.

1. The first two steps of the pipeline, which are performed to solve almost any NLP task, are segmentation (dividing text into sentences) and tokenization (dividing sentences into tokens, that is, separate words).

2. Calculation of the features of each token. The context-independent attributes of the token are calculated. This is a set of features that do not depend on the words adjacent to the token.

Looking to learn NLP and develop natural language processing applications? Do you want to create your own application or program for the voice assistant Amazon Alexa or Yandex Alice? In this article, we will talk about the directions of development and techniques that are used to solve NLP problems, so that it becomes easier for you to navigate (see more on this doctranslator website).

Natural language processing (hereinafter NLP - Natural language processing) is an area at the intersection of computer science, artificial intelligence and linguistics. The goal is to process and “understand” natural language to translate text and answer questions.

With the development of voice interfaces and chatbots, NLP has become one of the most important artificial intelligence technologies. But fully understanding and reproducing the meaning of language is an extremely difficult task, since human language has its own peculiarities.