Comparison of 6 top-level python NLP libraries!

Comparison of 6 top-level python NLP libraries!

http://blog.itpub.net/31509949/viewspace-2212320/

Natural language Processing (NLP) is becoming more and more popular today, especially in the context of deep learning and development. In the field of artificial intelligence, natural language processing (NLP) understands and extracts important information from text, and further data training based on text data, its main tasks include speech recognition and generation, text analysis, emotion analysis, machine translation and so on.

In the past few decades, only those experts who are proficient in language education can engage in natural language processing. In addition to their knowledge of mathematics and machine learning, they are proficient in a number of key language concepts. Now, we can use the compiled Natural Language Processing (NLP) library. Their main purpose is to simplify text preprocessing so that we can focus on building machine learning models and hyper-parameter trimming.

There are many tools and libraries that can solve natural language processing (NLP) problems. We now want to provide an overview and comparison of the most popular and helpful natural language processing libraries for users based on experience. Users should be aware that all of the tools and libraries we have introduced have only partially overlapping tasks. Therefore, it is sometimes difficult to compare them directly. We'll cover some of the features and compare the natural Language Processing (NLP) libraries that people might commonly use.

General overview

· NLTK (Python Natural Language Toolkit) is used for tasks such as tagging, word-back, stemming, parsing, POS labeling, and so on. The library has tools for almost all NLP tasks.

· Spacy is the main competitor of NLTK. These two libraries can be used for the same task.

· Scikit-learn provides a large library for machine learning. It also provides tools for text preprocessing.

· Gensim is a toolkit for topic and vector space Modeling, document collection similarity.

· The general task of the Pattern library is to act as a web mining module. Therefore, it only supports natural language processing (NLP) as a secondary task.

· Polyglot is another Python toolkit for Natural Language Processing (NLP). It is not very popular, but can also be used for various NLP tasks.

To make the comparison more intuitive, a table showing the pros and cons of each NLP library is listed below:

Conclusion

In this article, we compare some of the features of several popular natural language processing libraries. While most of them provide tools for overlapping tasks, some can use unique methods to solve specific problems. Of course, the most popular software packages currently in the NLP Library are NLTK and spacy. They are major rivals in the NLP field. In our view, the difference between them lies in the different ways of solving the problem.

NLTK is more academic. The user can use it to try different methods and algorithms to combine them together.

Instead, Spacy provides an out-of-the-box solution for each issue. The user does not have to consider which method is better: The writer of spacy has solved the problem. In addition, the Spacy executes very fast (several times faster than NLTK). But one drawback of spacy is that the number of languages supported is limited. But the number of languages it supports will continue to increase.

So, we think that Spacy is the best choice for users in most cases, but if users want to try something special, they can use NLTK.

Although the two libraries are popular, there are many different options, and the selection of the NLP Toolkit depends on the specific issues that the user must address.

Comparison of 6 top-level python NLP libraries!