8 Open-source NLP Tools You Should Try

Innovative technologies like voice assistants, predictive text, autocorrect, chatbots, and others have rapidly evolved in recent years, and the force behind it is Natural Language Processing (NLP).

NLP is a sub-field of Artificial Intelligence, which aims to emulate human intelligence and focuses on the interactions between computers and human language.

It typically allows computers to process and carefully analyze massive
amounts of natural language data.

Through effective implementation of NLP, one can naturally access relevant information in just seconds. Several businesses have implemented this technology by building customized chatbots, voice assistants and using their optical character & text simplification techniques to reap maximum benefits.

To help the businesses, there are several open-source NLP tools available which businesses can utilize according to their specific
requirements.

These open-source tools will not only help businesses to systemize the unstructured text but will also combat several other problems.

Below are the open-source NLP toolkit platforms anyone can use :

1. Natural Language Toolkit (NLTK)

It is an open-source platform used for python programming. It gives over 50 corpora and lexical resources like WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging,
parsing, and semantic reasoning, wrappers for industrial-strength NLP
libraries.

NLTK is appropriate for linguists, engineers, students, educators, researchers, etc., and is available for Windows, Mac OS X, and Linux.

2. SpaCy

SpaCy is another open-source library and typically comprises pre-trained statistical models and word vectors that support over 60 languages. Licensed under MIT, anyone can use it commercially. SpaCy supports custom models in PyTorch, TensorFlow, and other frameworks.

The main USP of SpaCy is Named Entity Recognition, part-of-speech tagging, dependency parsing, sentence segmentation, text classification, lemmatization, morphological analysis, entity linking, and others.

3. OpenNLP

OpenNLP supports the tasks such as tokenization, sentence segmentation, part-of-speech tagging, named entity extraction, chunking, parsing, language detection, and coreference resolution. Apart from this, it additionally includes maximum entropy and perceptron-based machine learning.

3. CoreNLP

It is another open-source platform which is developed by the Stanford NLP group as a possible solution for NLP in Java. It is currently supporting six languages (Arabic, Chinese, English, French, German, Spanish).

The USP of CoreNLP is sentence boundaries, parts-of-speech, named entities, numeric and time values, dependency and constituency parses, coreference, sentiment, quote attributions, and relations.

5. AllenNLP

Allen is an open-source platform based on PyTorch. It is a deep learning library for NLP used for the tasks such as responding to questions, semantic role labeling, textual entailment, text to SQL.

6. Flair

Like AllenNLP, Flair is also built on PyTorch. This open-source platform allows using the platform’s state-of-art NLP models of text, such as Named Entity Recognition (NER), part-of-speech tagging, sense disambiguation and
classification.

It includes simpler interfaces where one can combine various words and document embeddings.

7. SparkNLP

SparkNLP is an open-source platform that gives over 200 pre-trained pipelines and models supporting more than 40 languages. SparkNLP supports transformers like BERT, XLNet, ELMO and carries out accurate and clear annotations for NLP.

8. Gensim

Gensim is a free and open-source python library uniquely designed to process raw texts using quality machine learning algorithms. It is used for topic modeling, document indexing.

The USP of the platform is tokenization, part-of-speech tagging, named entity recognition, spell checking, multi-class text classification, multi-class sentiment analysis.

Natural Language Processing is a crucial and revolutionary technology. I expect this technology to flourish in the possible future with the successful adoption of more personal assistants, dependencies on smartphones, and the evolution of Big Data.