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This article mainly introduces which NLP libraries are in Python, which are very detailed and have certain reference value. Friends who are interested must finish reading them.

Natural language processing (NLP) is becoming more and more popular nowadays, especially in the context of deep learning development. In the field of artificial intelligence, natural language processing (NLP) understands and extracts important information from text, and carries out 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 some 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 fine-tuning hyperparameters.

There are many tools and libraries that can solve natural language processing (NLP) problems. We now hope to summarize and compare the most popular and helpful natural language processing libraries based on experience. Users should be aware that all the tools and libraries we introduce have only partially overlapping tasks. Therefore, it is sometimes difficult to compare them directly. We will introduce some features and compare natural language processing (NLP) libraries that people may use.

General overview

NLTK (Python Natural language Toolkit) is used for tasks such as tokenization, morphological restoration, drying, parsing, POS tagging, and so on. The library has tools for almost all NLP tasks.

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

Scikit-learn provides a large library for machine learning. In addition, tools are provided for text preprocessing.

Gensim is a toolkit for topic and vector space modeling and document set 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 an auxiliary task.

Polyglot is another Python toolkit for Natural language processing (NLP). It is not very popular, but it can also be used for a variety of NLP tasks.

To make the comparison more intuitive, here are the tables that show the advantages and disadvantages of each NLP library:

Conclusion

In this paper, we compare some functions 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 packages in the NLP library today are NLTK and Spacy. They are the main competitors in the NLP field. In our opinion, the difference between them lies in the different ways to solve the problem.

NLTK is more academic. Users can use it to try different methods and algorithms and combine them. Instead, Spacy provides an out-of-the-box solution for each problem. Users don't have to think about which approach is better: the writers of Spacy have solved this problem. In addition, Spacy is 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 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 choice of the NLP toolkit depends on the specific problems that users have to solve.

ActiveWizards is a team of data scientists and engineers who specialize in data projects (big data, data science, machine learning, data visualization). Its core areas of expertise include data science (research, machine learning algorithms, visualization and engineering), data visualization (d3.js, Tableau and others), big data engineering (Hadoop, Spark, Kafka, Cassandra, HBase, MongoDB, etc.), and data-intensive Web application development (RESTful API, Flask, Django, Meteor).

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