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This article introduces the knowledge of "what are the Python natural language packs". In the operation of practical cases, many people will encounter such a dilemma. Next, let the editor lead you to learn how to deal with these situations. I hope you can read it carefully and be able to achieve something!

Conqueror: NLTK

When talking about the nlp library in Python, you have to mention NLTK. It is the most famous Python NLP library, and it has made an incredible breakthrough in this field. NLTK is responsible for conquering many problems of text analysis, and NLTK is also popular in the field of education and research. On its website, NLTK calls itself "an amazing library of natural languages."

In our experience, the key word is "play". NLTK is available in 50 corpora and dictionaries, 9 stemmers, and dozens of optional algorithms. It is a theme park for academic researchers.

However, this is also a major disadvantage of NLTK. It is heavy and slippery, and it has a steep learning curve. The second major disadvantage is slowness rather than production readiness.

Prince: textblob

Textblob sits on the shoulders of giants, and another library similar to this is Pattern. In fact, we recommend textblob instead of Pattern.

Textblob enables text processing to provide an intuitive interface that simplifies NLTK. Because it has a mild learning curve and amazing features, it has become the most popular natural language library of Python.

For example, suppose you want to find the emotional score of a text. You can:

From textblob import TextBlobopinion = TextBlob ("EliteDataScience.com is dope.") opinion.sentiment

By default, the emotion analyzer is from the pattern library of patternanalyzer. But what if you want to use naive Bayesian analysis? You can easily switch to an analyzer trained with nltk.

From textblob import TextBlobfrom textblob.sentiments import NaiveBayesAnalyzeropinion = TextBlob ("EliteDataScience.com is dope!", analyzer=NaiveBayesAnalyzer ()) opinion.sentiment

Textblob is a simple and interesting library that makes text analysis a relaxing and enjoyable thing. We can use textblob against all the initial prototypes of NLP.

Mercenaries: Stanford corenlp

The Stanford corenlp library is a suite of tools for the production preparation phase of natural language analysis. It includes part of speech (POS) tagging, entity parsing, pattern recognition, learning and so on. "mercenary" is actually written in Java rather than Python.

Many organizations use corenlp to implement their products. It can almost accurately support several major languages.

Usurper: Spacy

Spacy is a new thing, it caused a sensation in the industry as soon as it was launched. The market is targeted at the Python natural language library of operational efficiency.

Spacy is very light and small, and its philosophy is to propose only one algorithm (the best one) for each problem. You don't have to make a choice, you can focus on being productive.

The kernel is Cpython, so Spacy runs very fast. Its main disadvantage is that it only supports English at present.

Spacy is very new, so its support community is not as large as other libraries. However, its NLP approach is so compelling that it seems that NLTK can be replaced in the future.

If you are building a new application or retrofitting an old one (you only need English support), then we strongly recommend using Spacy.

General: gensim

Last but not least, we have the gensim library. Gensim does not solve all nlp areas, it only does part of it and does it well. You won't send your admiral to a land scene, and in the same way you won't use gensim to solve all areas and scenarios of nlp.

Gensim is a good optimization library for topic modeling and document similarity analysis. Of the Python NLP libraries listed here, it is the most professional.

Even so, this is a valuable tool to add your tracks. Its topic modeling algorithms, such as the potential Dirichlet allocation (LDA) implementation, are the best of all libraries. In addition, it is robust, efficient and scalable.

In addition, subfield semantic analysis (or topic modeling) is one of the most exciting areas of modern natural language processing.

This is the end of "what is the Python Natural language Pack"? thank you for reading. If you want to know more about the industry, you can follow the website, the editor will output more high-quality practical articles for you!

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