Top 10 Natural Language Processing Tools | Text Analysis

The Internet is the big source of unstructured text data that needs advance optimized text analysis frameworks and Natural Language Processing Tools.

The huge amount of data on the web is unstructured and it needs the best processing solution whereas the natural language processing tools are making the text analysis on big unstructured data very easy.

There is a wide number of documents and text data is generating every day in business and they need natural language processing applications or NLP tools to get insight from that data.

Similarly, an intense amount of searches happen every day about free NLP tools, online NLP tools, etc. and most of the developers are searches for NLP tools in the Python and Natural Language understanding Book etc. that shows NLP is a very well know the subject.

For this reason, in this article, we are going to discuss the top well-known and highly using natural language processing tools in the industry.

Top Natural Language Processing Tools

The below Natural language processing tools are python programming-based libraries that need coding expertise and some are independent software applications with less coding expertise to work for NLP.

1. NLTK

NLTK is a leading tool and programming library which is a built-in python language used to implements and interpret human language data.

It utilizes WordNet the lexical database for multiple language data processing and it is generally used to find the semantic relations and matches between words of over 200 languages.

NLTK is a highly useful tool for more than 50 corpora (corpus) and lexical resources to perform tokenization, parsing, stemming, tagging, classification on text data.

This tool can do a wide variety of Natural Language Processing tasks in Python programming to do the easy way of text classification, language interpretation or getting estimates from the text, and more.

2. SpaCy

After NLTK there is another best python programming library for efficient natural language processing called SpaCy and it supports more than 70 languages for NLP operations.

SpaCy has inbuilt visualizers for syntax and NER this can ease to deliver model packaging, deployment, and workflow management.

It is a very robust python library and proposes rigorously evaluated accuracy for NLP development and for NLP frameworks.

It is simply extensible with custom components and attributes Support for custom models in PyTorch, TensorFlow, and other NLP frameworks

3. Stanford Core NLP

Stanford CoreNLP’s basic name is CoreNLP which originally developed for natural language processing using Java language.

It enables users to receive accurate semantic annotations from text, including tokens, sentence boundaries, and the parts of speech.

It can give the named entities, sentiment, relations, numeric and time values, dependency and constituency parse, coreference, quote attributions, etc.

The Stanford CoreNLP currently recommends 6 languages for NLP operation like English, Spanish, French, German, Arabic, and Chinese.

A significant part of the Stanford CoreNLP library is situated in the pipeline that uses raw text and runs a series of NLP annotators on the text data to get produces a final set of annotations.

4. Apache OpenNLP

Apache OpenNLP is Actively Developed for natural language processing and machine learning operation and used for Largely scalable processing of unstructured text data

Its uses for multiple languages to perform NLP operations like sentences and word detection, part of speech, text tokenization, text data parsing, text lemmatize, and chunker, etc.

5. AllenNLP

It is the prebuild advanced natural language process library which uses RNN-LSTM a deep learning approach for NLP and NLP Software.

The Allen Institute for Artificial Intelligence builds the AllenNLP library using the Pytorch library approach which means its created based on the Pytorch module for multiple linguistic operations.

It helps to perform several NLP tasks like Sentiment analysis, Text classification, Question Answer generation, text to the SQL operation, etc.

AllenNLP development needs three steps to build a classifier those are Define the DatasetReader, then Define the RNN Model, and at last Setuping Your Config Files, etc.

In simple words, it is easy to design and developed using recurrent neural network the deep learning approach to make the unstructured and text analysis very easy.

6. GenSim

Gensim is the advanced NLP library design in python programming for several NLP operations and it is highly utilized for online natural language processing software.

It supports different text analysis tasks like sentiment finding from data, topic modeling, corpse creation from text, and so on.

Gensim is an online text processing and unstructured text analysis library developed based on python and Cython programming languages.

It can do a lot more text-related operations on unstructured big data like word2text, doc2vec, LSA, LDA, and others.

7. TextBlob Library

TextBlob is the easy and fastest implementation library in Python to execute and process unstructured text data efficiently.

It supports a variety of text processing techniques for text analysis like text Tokenization, Sentiment Analysis, Part-of-speech Tagging, Noun Phrase Extraction, and much more.

Textblob employed for simple and easy to execute an operation for text data in any application and web-based platform using API support.

8. Intel NLP Architect

Intel NLP Architect is an open-source natural language processing toolkit and library invented in python to well-optimized NLP techniques and deep neural network processes.

It can prepare the various NLP tasks like data-manipulation, text processing, documents and metrics operations, and a bunch of advanced text analysis operations.

It follows the model-oriented design to build up a natural language processing task that contains command-line model training and executing using CLI subsystem with execution methods.

Intel NLP library in python manages API utilization for interfacing of code and it utilizes multiple customizing processes for model building end execution.

It is designed and developed for natural language processing with the help of neural network toolkits and deep learning frameworks or tools like PyTorch, TensorFlow, and Dynet.

9. IBM Watson

IBM Watson is the AI-based system and Studio used for several machine learning and natural language process jobs and it applying deep AI for text and data for the fastest processing.

It provides a Watson natural language understanding service for commercial use to perform text analysis in a more advanced way.

IBM Watson is the more reliable advanced Natural language processing platform that gives strong-insight and highly accurate results from unstructured data comparable to other NLP tools.

It helps to obtain the right NLP insights from text data like sentiments analysis, tokenization, Bag-of-words model, etc. and it can deploy on any cloud platform for big data analytics.

10. Google Cloud NLP API

The Google Cloud program provides a Natural Language processing service like google Natural Language API and AutoML Natural Language platforms for text analysis.

These NLP services use as a tool for several Natural Language Processing tasks like understanding the sentiment and customer conversations from text data, content classification, etc.

Google Natural Language API used for various text-related operations including syntax analysis, sentiment analysis, entity analysis, content classification, and so on.

Similarly, the AutoML Natural Language platform enables the user to perform content categorization, sentiment analysis, document classification especially pdf, and other data modeling benefits.

Google Natural Language processing platforms both are highly used for NLP Google platforms are very efficient to use than other tools without any coding knowledge and free to start and minimum fees for huge data.

Conclusion

There is a lot more invention is going on in artificial intelligence and Natural language processing to improve speech and languages recognitions in more optimized ways.

In the world, there is an intense number of languages are available in different geolocations, and dealing with the area-wise data needs more reliable technology for language translation.

If you want to explore more natural language processing tools you can check these open-source tools like PyTorch-NLP, Amazon Comprehend, Textacy, CogCompNLP, etc.

The NLP tools are free to use because most of them are open source and there is an immense community of natural language processing, text analysis, unstructured data analysis, and Artificial intelligence that makes unstructured data and Text analysis easy.

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