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This article mainly introduces "what are the open source tools handled by Python, Node.js and Java languages". In daily operations, I believe many people have doubts about the open source tools handled by Python, Node.js and Java languages. Xiaobian consulted all kinds of materials and sorted out simple and easy-to-use methods of operation. I hope it will be helpful to answer the doubts about "what are the open source tools handled by Python, Node.js and Java languages?" Next, please follow the editor to study!

Python tool

Natural language Toolkit (NLTK)

There is no doubt that the Natural language Toolkit (NLTK) is the most functional tool I have ever studied. It almost implements most of the functional components of natural language processing, such as classification, tokenization, word drying, tagging, word segmentation and semantic reasoning. Each has a variety of different implementations, so you can choose specific algorithms and methods. It also supports different languages. However, it represents all the data as a string, which may be convenient for some simple data structures, but it may be a little difficult to use some advanced features. Its usage documentation is a bit complicated, but there are also a lot of usage documents written by other people, such as this great book. Compared with other tools, this tool library is a bit slow. But overall, this toolkit is very good and can be used in experiments, explorations, and practical applications that require specific combinations of algorithms.

SpaCy

SpaCy may be the main competitor to NLTK. It is faster than NLTK in most cases, but there is only one implementation of each functional component of natural language processing in SpaCy. SpaCy simplifies the application building interface by representing everything as an object rather than a string. It also facilitates its integration with a variety of frameworks and data science tools, making it easier for you to understand your text data. However, SpaCy does not support multiple languages like NLTK. It does have a simple interface, a simplified set of options and complete documentation, as well as a variety of neural network models for language processing and analysis of various components. Overall, this is a good tool for new applications that need to perform well in production and do not require specific algorithms.

TextBlob

TextBlob is an extension library of NLTK. You can use the functions of NLTK in an easier way through TextBlob, and TextBlob also includes functions in the Pattern library. If you are just starting to learn, this will be a good tool for applications in production environments that do not require too much performance. In general, TextBlob is suitable for any scenario, but it is especially good for small projects.

Textacy

This tool is the best name I have ever used. First reread "ex" and then bring out "cy", and try to read "Textacy" several times. It is not only a good name to read, but also a good tool in itself. It uses SpaCy as its core natural language processing function, but it does a lot of work before and after the process. If you want to use SpaCy, it's best to use Textacy so that you can handle different kinds of data without having to write additional code.

PyTorch-NLP

PyTorch-NLP has been around for only a year, but it already has a large community. It is suitable for rapid prototyping development. When there is the latest research, or when large companies or researchers come up with other tools to perform novel processing tasks, such as image conversion, it will be updated. Overall, PyTorch is aimed at researchers, but it can also be used in prototyping or in initial production loads using state-of-the-art algorithms. The library created on this basis is also worth studying.

Node.js tool

Retext

Retext is part of the Unified collection. Unified is an interface that integrates different tools and plug-ins so that they can work efficiently. Retext is one of the three grammars used in Unified tools, and the other two are Remark for Markdown and Rehype for HTML. This is a very interesting idea and I am glad to see the development of this community. Retext doesn't involve a lot of underlying technology, and it's more about using plug-ins to do what you want to do in a NLP task. Spell checking, glyph repair, mood detection, and enhanced readability can all be done with simple plug-ins. In general, this tool and community is a good choice if you don't want to understand the underlying processing technology and want to accomplish your task.

Compromise

Compromise is obviously not the most complex tool, and if you are looking for the most advanced algorithm and the most complete system, it may not be suitable for you. However, if you want a tool that has good performance, a wide range of features, and can run on the client side, Compromise is worth a try. Overall, its name ("compromise") is accurate because the author pays more attention to small packages with more specific functions, while there is a compromise in terms of functionality and accuracy, which benefit from the user's understanding of the environment.

Natural

Natural contains most of the functions of a regular natural language processing library. It mainly deals with English texts, but also includes some other languages, and its community welcomes support for other languages. It can perform tokenization, word stemming, classification, speech processing, word frequency-inverse document frequency calculation (TF-IDF), WordNet, character similarity calculation and some transformations. It is comparable to NLTK because it wants to include everything in one package, but it is easier to use and does not necessarily focus on research. Overall, this is a very complete library that is still under active development, but you may need to know more about the underlying implementation to be fully effective.

Nlp.js

Nlp.js builds on several other NLP libraries, including Franc and Brain.js. It provides a good interface for many NLP components, such as classification, emotion analysis, word stemming, named entity recognition and natural language generation. It also supports some other languages and can help you when dealing with languages other than English. In short, it is a good general-purpose tool and provides a simplified interface to call other tools. This tool may be used in your application for a long time before you need a more powerful or flexible tool.

Java tool

OpenNLP

OpenNLP is managed by the Apache Foundation, so it can be easily integrated into other Apache projects, such as Apache Flink, Apache NiFi, and Apache Spark. This is a general-purpose NLP tool that contains common functionality in all NLP components and can be used either from the command line or as a package into the application. It also supports many languages. OpenNLP is a very efficient tool with many features, and it is a good choice if you use Java to develop production environment products.

Stanford CoreNLP

Stanford CoreNLP is a toolset that provides statistical NLP, deep learning NLP, and rule-based NLP functions. This tool is also available in many other programming languages, so it can be used separately from Java. It is an efficient tool created by high-level research institutions, but it may not be the best in a production environment. This tool uses dual licenses and has specific licenses that can be used for commercial purposes. In short, it is a great tool in research and experiments, but it may incur some additional costs in the production system. Readers may be more interested in its Python version than the Java version. Similarly, one of the best machine learning courses on Coursera is offered by Professor Stanford. Click here to visit other good resources.

CogCompNLP

CogCompNLP, a tool developed by the University of Illinois, also has a Python version of similar functionality. It can be used to process text, including local processing and remote processing, and can greatly relieve the pressure on your local device. It provides many processing functions, such as tokenization, part of speech tagging, sentence breaking, named entity tagging, word form restoration, dependency analysis and semantic role tagging. It is a good research tool, and you can explore its different functions. I'm not sure it's suitable for a production environment, but if you use Java, it's worth a try.

At this point, the study on "what are the open source tools for Python, Node.js, and Java language processing" is over. I hope to be able to solve your doubts. The collocation of theory and practice can better help you learn, go and try it! If you want to continue to learn more related knowledge, please continue to follow the website, the editor will continue to work hard to bring you more practical articles!

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