You can surely understand me if I say something! But what about a computer? Can it understand what I am saying? Normally the answer is no because computers are not meant to speak or understand human language. But Natural Language Processing is the field that enables computers to not only understand what humans are saying but also reply! NLP is a subcategory of artificial intelligence that aims to teach the human language with all its complexities to computers. This is so that machines can understand and interpret our language to eventually understand human communication in a better way.
But the question is how is NLP actually implemented? Well, there are many libraries that provide the foundation of Natural Language Processing. These libraries have various functions that can be used to make computers understand natural language by breaking the text according to its syntax, extracting the important phrases, removing extraneous words, etc. This article particularly provides the popular NLP libraries in Python. So check out these libraries and who knows, you may even use them to create your own Natural Language Processing project!
1. Natural Language Toolkit (NLTK)
The Natural Language Toolkit is the most popular platform for creating applications that deal with human language. NLTK has various different libraries for performing text functions ranging from stemming, tokenization, parsing, classification, semantic reasoning, etc. The most important thing is that the NLTK is free and open-source and it can be used by students, professionals, linguists, researchers, etc. This toolkit is a perfect option for people just getting started into natural language processing but it is a bit slow for industry-level projects. However, it does have a steep learning curve so it might take some time to get completely familiar with it.
TextBlob is a Python library that is created for the express purpose of processing textual data and handling natural language processing with various capabilities such as noun phrase extraction, tokenization, translation, sentiment analysis, part-of-speech tagging, lemmatization, classification, spelling correction, etc. TextBlob is created on the basis of NLTK and Pattern and so can be easily integrated with both these libraries. All in all, TextBlob is a perfect option for beginners to understand the complexities of NLP and creating prototypes for their projects. However, this library is too slow for usage in industry level NLP production projects.
Gensim is a Python library that is specifically created for information retrieval and natural language processing. It has many algorithms that can be utilized regardless of the corpus size where the corpus is the collection of linguistic data. Gensim is dependent on NumPy and SciPy which are both Python packages for scientific computing, so they must be installed before installing Gensim. This library is also extremely efficient and it has top-notch memory optimization and processing speed.
spaCy is a natural language processing library in Python that is designed to be used in the real word for industry projects and gaining useful insights. spaCy is written in memory-managed Cython which makes it extremely fast. Its website claims it is the fastest in the world and also the Ruby on Rails of Natural Language Processing! spaCy provides support for various features in NLP such as tokenization, named entity recognition, Part-of-speech tagging, dependency parsing, sentence segmentation using syntax, etc. It can be used to create sophisticated NLP models in Python and also integrate with the other libraries in the Python eco-system such as TensorFlow, scikit-learn, PyTorch, etc.
Polyglot is a free NLP package that can support different multilingual applications. It provides different analysis options in natural language processing along with coverage for lots of languages. Polyglot is extremely fast because of its basis in NumPy, a Python package for scientific computing. Polyglot supports various features inherent in NLP such as Language detection, Named Entity Recognition, Sentiment Analysis, Tokenization, Word Embeddings, Transliteration, Tagging Parts of Speech, etc. This package is quite similar to spaCy and an excellent option for those languages that spaCy does not support as it provides a wide variety.
CoreNLP is a natural language processing library that is created in Java but it still provides a wrapper for Python. This library provides many features of NLP such as creating linguistic annotations for text which have token and sentence boundaries, named entities, parts of speech, coreference, sentiment, numeric and time values, relations, etc. CoreNLP was created by Stanford and it can be used in various industry-level implementations because of its good speed. It is also possible to integrate CoreNLP with the Natural Language Toolkit to make it much more efficient than its basic form.
Quepy is a specialty Python framework that can be used to convert questions in a natural language to a query language for querying a database. This is obviously a niche application of natural language processing and it can be used for a wide variety of natural language questions for database querying. Quepy currently supports SPARQL which is used to query data in Resource Description Framework format and MQL is the monitoring query language for Cloud Monitoring time-series data. Supports for other query languages are not yet available but might be there in the future.
Vocabulary is basically a dictionary for natural language processing in Python. Using this library, you can take any word and obtain its word meaning, synonyms, antonyms, translations, parts of speech, usage example, pronunciation, hyphenation, etc. This is also possible using Wordnet but Vocabulary can return all these in simple JSON objects as it normally returns the values as those or Python dictionaries and lists. Vocabulary is also very easy to install and its extremely fast and simple to use.
PyNLPl is a natural language processing library that is actually pronounced as “Pineapple”. It has various different models to perform NLP tasks including pynlpl.datatype, pynlpl.evaluation, pynlpl.formats.folia, pynlpl.formats.fql, etc. FQL is the FoLiA Query Language that can manipulate documents using the FoLiA format or the Format for Linguistic Annotation. This is quite an exclusive character set of PyNLPl as compared to other natural language processing libraries.
Pattern is a Python web mining library and it also has tools for natural language processing, data mining, machine learning, network analysis, etc. Pattern can manage all the processes for NLP that include tokenization, translation, sentiment analysis, part-of-speech tagging, lemmatization, classification, spelling correction, etc. However, just using Pattern may not be enough for natural language processing because it is primarily created keeping web mining in mind.
These natural language programming libraries are the most popular in Python. There are many other libraries in different programming languages for NLP as well such as Retext and Compromise in Node, OpenNLP in Java, and some libraries in R as well such as Quanteda, Text2vec, etc. However, this article particularly focuses on the NLP libraries in Python as it is the most popular programming language in Artificial Intelligence and also the most frequently used for industrial projects.