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In this article Xiaobian for you to introduce in detail "what are the open source tools for web natural language processing", the content is detailed, the steps are clear, and the details are handled properly. I hope this article "what are the open source tools for web natural language processing" can help you solve your doubts.

1. Python tool

1. Natural language Toolkit (NLTK)

The Natural language Kit (NLTK) is the most fully functional tool. It implements almost any NLP component you need, such as classification, tagging, parsing and semantic reasoning. And each method usually has multiple implementations, so you can choose the exact algorithm or method you want to use. It also supports multiple languages. But it represents all the data as a string, which is good for a simple architecture, but it is difficult to use some advanced features. Compared with other tools, its development is a bit slow. Overall, this is a good toolkit for experiments, explorations, and applications that require specific combinations of algorithms.

2. SpaCy

SpaCy is the main competitor to NLTK. It is faster in most cases, but there is only one implementation for each NLP component. In addition, it represents everything as objects rather than strings, which simplifies the interface for building applications. This also helps it integrate with many other frameworks and data science tools, so you can do more after you have a better understanding of text data. However, SpaCy does not support as many languages as NLTK. It does have a simple interface, a set of simplified choices and excellent documentation, and multiple neural models of various components of language processing and analysis. In general, this is a good tool for new applications that need high performance in production and do not require specific algorithms.

3. TextBlob

TextBlob is an extension of NLTK. Many NLTK functions can be accessed in a simplified way through TextBlob, and TextBlob also includes functions from the Pattern library. If you are just getting started, this may be a good tool for learning, and it can be used in production for applications that do not need to be overexecuted. In general, TextBlob is used everywhere and is ideal for small projects.

4. Textacy

Textacy is also a great tool. It uses SpaCy as its core NLP function, but it handles a lot of work before and after processing. If you plan to use SpaCy, you can also use Textacy, so you can easily introduce many types of data without writing additional help code.

5. PyTorch-NLP

PyTorch-NLP has been around for some time, but it already has a large community. It is a rapid prototyping tool. It is also often updated through research, and companies and researchers have released a number of other tools for a variety of amazing processing, such as image transformation. Overall, PyTorch is aimed at researchers, but it can also be used for prototypes and initial production workloads, and provides progressive algorithms. The libraries created on top of it may also be worth studying.

II. Node tools

6. Retext

Retext is part of unified collective. Unified is an interface that allows multiple tools and plug-ins to integrate and work together effectively. Retext is one of the three grammars used by the unified tool; the others are Markmark's Remark and HTML's Rehype. Instead of exposing many of its underlying technologies, Retext uses plug-ins to achieve results that you might target with NLP. It's easy to do things like check spelling, fix typesetting, check emotions, or make sure simple plug-ins can read text. In general, this is an excellent tool and community if you only need to do some work without knowing everything in the underlying process.

7. Compromise

Compromise is certainly not the most complex tool. If you are looking for an advanced algorithm or the most complete system, this may not be for you. However, if you want a high-performance tool with a wide range of features that can be run on the client, you should take a look at Compromise.

8. Natural

Natural contains most of the features you might expect in a general NLP library. It focuses mainly on English, but some other languages are already available and the community is open to other contributions. It supports tagging, stemming, classification, pronunciation, term frequency-inverse document frequency, WordNet, string similarity and some variations. It may be comparable to NLTK*** because it tries 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, but it is still under active development and may require additional low-level implementation knowledge to be fully effective.

9. Nlp.js

Nlp.js builds on several other NLP libraries, including Franc and Brain.js. It provides a good interface for many components of NLP, such as classification, emotion analysis, stemming, named entity recognition and natural language generation. It also supports multiple languages, which can be useful if you plan to use languages other than English. Overall, this is a great general-purpose tool that simplifies the interface with several other tools. This may be used in your application for a long time before you need more powerful or flexible features.

III. Java tools

10. OpenNLP

OpenNLP is hosted by Apache Foundation, so it is easy to integrate it into other Apache projects, such as Apache Flink,Apache NiFi and Apache Spark. It is a general-purpose NLP tool that covers all the common processing components of NLP and can be used as a library from the command line or applications. It also widely supports multiple languages. Overall, OpenNLP is a powerful tool with many features, and if you use Java, you can prepare for production workloads.

11. StanfordNLP

Stanford CoreNLP is a set of tools that provide statistical NLP, deep learning NLP, and rule-based NLP functions. Many other programming language bindings have been created, so you can use this tool outside of Java. It is a very powerful tool created by elite research institutions, but it may not be a choice for production workloads. This tool has a dual license and a special license for commercial use. Overall, this is a good research and experimental tool, but it may incur additional costs in the production system.

12. CogCompNLP

CogCompNLP, developed by the University of Illinois, has a Python library with similar functionality. It can be used for text processing on local or remote systems, which eliminates a huge burden on local devices. It provides processing functions such as tagging, part-of-speech tagging, chunking, named entity tagging, morphological restoration, dependency and selection analysis, and semantic role tagging. Overall, this is a good research tool, and it has a lot of components you can explore. I'm not sure it's good for production workloads, but if you're going to use Java, it's worth a try.

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