7 Open Source Tools for Natural Language Processing

7 Open Source Tools for Natural Language Processing

The majority of the applications that dominate our lives are powered by Natural language processing. The advancements in this field have only increased in recent years with the presence of robots, voice assistants, and other bots. It helps in processing and analyzing large amounts of language data. Businesses rely upon natural language processing for creating chatbots and other speech/text applications.

Thus, the requirements for Natural Language Processing open-source tools are at an all-time high. They are easily customizable according to the needs of the developers. These tools along with assisting in analyzing the required information from unstructured data also ease solving the problem of text analysis. Here are 7 open source tools for Natural language processing that will help computer science and artificial intelligence enthusiasts to start their journey with NLP.

Best Solutions for NLP

  • spaCy

spaCy is an open-source library for natural language processing. It is used for python and cython. Yet, however fast and smooth, it does not support many languages as compared to other tools for NLP. spaCy boasts of a user-friendly interface with a simplified set of choices and includes various components of natural language processing and analysis. It also has pre-trained statistical models along with word vectors supporting over sixty languages. All in all, it is a trustworthy tool, built on the latest research, for applications that do not require a particular algorithm.

  • Natural Language Toolkit (NLTK)

This is one of the reliable tools when it comes to the implementation of any components of natural language processing. Natural Language Toolkit is primarily used for python language programming, though it supports many languages and is easy to use. It efficiently analyzes human languages and has many text processing libraries. It shows data in the form of strings, due to which advanced functionality becomes difficult to use. Its features include tokenization, semantic analysis, lexical corpus integration among others.

  • Stanford NLP
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Stanford NLP is a highly sought-after natural language processing tool as it offers rule-based NLP functionality, statistical NLP, and deep learning NLP as well. It is mainly used for Java but with the help of other programming language bindings, it can be used outside of Java, too. Created by a renowned institution, however powerful, this tool may not be a good use for production workloads. Standard NLP has many great features like language support for tokenization, named entity extraction, and parsing, to name a few. It is often updated with the latest research. Stanford NLP is a dual-licensed open-source tool having a special license for commercial use.

  • OpenNLP

OpenNLP is a great tool to process natural language text. It has gained popularity over a very short period of time. It can be leveraged to develop advanced text processing services apart from detecting language and segmenting sentences. OpenNLP can be used for research and experimentation, but it may increase additional costs. This might be a good tool to start your learning journey with. Also, it includes machine learning which is based on perceptron and provides straightforward annotations for natural language processing.

  • Retext

Retext, a part of the unified collective, allows the integration of several tools and plugins to work efficiently. It is a performant tool with a variety of functions. Some of its features are fixing typography, spell checking, and multi-class sentiment analysis. It is accurate and fast. Not only with speed recognition, but it also assists in text simplification. With a user-friendly design, one can access functions of Rextext in a very simplified manner. Overall, it is an amazing tool to get the job done without any hassles.

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If tools won’t be of sufficient help with your language assignment, you can use TheWordPoint to get a professional translation of any text. They will help with your academic work or any other translation need that may arise.

  • CogCompNLP

Built by the University of Illinois, CogCompNLP is an open-source python library to smoothly process text and ease the burden on your local device. It offers a wide range of functions – tokenization, part-of-speech tagging, named entities, etc. CogCompNLP can also be used for lemmatization, semantic role labeling, and textual entailment. The tool is extensively used by linguists, machine learning enthusiasts, and researchers.

  • Natural

Natural has mostly all the functions of a general natural language processing library. It primarily focuses on English, but other languages are supported as well, and the community is interested in additional contributions. Natural boasts of features such as term frequency-inverse document frequency, classification, stemming, and phonetics, etc. It is easier to use as compared to Natural Language Toolkit or other tools. Additionally, it is not much focused on research and includes all the functions in one package. All in all, this is a reliable tool for natural language processing but may need knowledge of the underlying process.

Final Words

This should be adequate to have you convinced to get started on your natural language processing journey. Hope this helps you in choosing the right set of NLP tools for your next project involving the use of text or voice-based applications. Check out the solutions we stated and take advantage of modern-day tech solutions. The aid of such tools can be invaluable for the best completion of your project as well as learning.