Top Natural Language Processing (NLP) libraries for Python

What is natural language processing (NLP)?

Natural Language Processing (NLP) is part of computer science and Artificial intelligence that helps to communicate between the computer(machine ) and humans through natural language. It makes a computer or machine to read and understand by stimulating the human natural language.

NLP is booming day by day because of the production of a large amount of data and also more unstructured data.

The basic task of natural language processing :

1. Tokenization: tokenization is a process of breaking of text into smaller meaningful elements called tokens.
2. Word Stemming and Lemmatization: The main focus of Stemming and Lemmatization is derived from the word form to its base root.
3. Part of speech (POS) Tagging: The main work of  POS tagging is to assign a label to each word with a respective grammatical component.
4. Chunking: Chunking is picking up small pieces of information and grouping them into a bigger one.
5. Stop Word Removal: stop words are the simple word that usually part of the grammatical structure of the sentence. This stop word removal help in sentiment analysis.
6. Name entity recognition: It is identifying the entities such as name, location, etc it is mostly found in unstructured data.

Application of NLP:

Sentimental analysis, chatbot,speech recognition, machine translation, spell checking, keyword search, Advertisement matching.

In this article, we will know about NLP libraries and from which libraries one can start in Natural Language Processing.

   Top  Natural Language Processing (NLP) libraries for Python are listed below :

1. Natural Language Toolkit (NLTK):

Natural Language Toolkit is well known and most popular python libraries used for natural language processing. It is free and opens sourced and available for Windows, Mac os, Linux operating system. It has almost 50 copras and related lexical resources. It provides an easy to use interface. NLTK comes with the text processing libraries for sentence detection, tokenization, lemmatization, stemming, parsing, chunking, and POS tagging. It provides the particle introduction to programming for language processing.

2. SpaCy :

SpaCy is open-source natural language processing which is written in Cython (Cython is an extension of python design to give c like performance ). It is designed explicitly for production use -where we can develop the application which can process and understand a huge volume of data. SpaCy comes with a pre-trained statistical model and word vector and supports tokenization for many languages. It supports Windows, Linux, Mac os, and python environments such as pip, conda, etc. it can pre-process text for deep learning. It includes almost every feature such as

Tokenization, sentence segmentation, word vector name entity recognition, and many more. In addition to this, it also includes the optimization of GPU operation.

3. Pattern :

The pattern is another popular natural language processing python library. It can be a powerful tool that is used in both scientific and non-scientific. It allows part of speech tagging, sentiment analysis, vector space modeling,SVM, clustering, wordnet, and n-gram. It is as simple and straight forward syntax, the syntax is such that they can be self-explanatory. The pattern supports Python 2.7 and Python 3.6. It is maintained by CLIPS  and also has good documentation of it. It includes DOM parser and we crawl and offer to access to use APIs. It is one of the datamini9ng libraries which is used to parse and crawl a variety of sources such as Google, Twitter, and many more.

4. Polyglot :

Polyglot is one of the favorites because it offers a broad range of analysis, supports multilingual application and impressive language coverage. Polyglot depends on Numpy and Libicu-dev.

The feature includes tokenization,language detection, named entity recognition, part of speech tagging, sentiment analysis, word embedding, etc. Polyglot requests the usage of dedicated command in the command line through the pipeline mechanisms. It is great language coverage and fasts.

5. Textblob :

Textblob if for both python 2 and Python 3 library design for processing textual data. The natural language processing task such as part of speech tagging, noun phrase extraction, sentiment analysis, classification, translation, WordNet, integration, parsing, word inflection, add new models or language extensions, and more. It also provides a simple API and great API call for Natural Language processing. It helps in providing access to common text processing operations through a familiar interface. It has the concept of a Textbolb object that can be treated as a Python string that is trained in natural language processing. If anyone wants to put the first step toward NLP with python one should use this library. It helps design prototype. It has some features such as it can handle large text collection, memory use optimization, high processing speed.

6. PyNLPI :

PyNLPI is a python library for natural language processing and has a custom made python module NLP task. The outstanding feature of NLPI has an extensive library for working with Format for linguistic Annotation. It consists of different nodules and packages each useful for both standard and advanced natural language processing tasks. We can use NLPI for basic NLP tasks like extraction of n-grams and frequency lists and frequency lists and to build a simple language model, and it also has more complex data types and advanced NLP tasks.

7. Core NLP :

Core NLP provide set of human language technology tool It provides the linguistic analytic tool for a piece of text tool. Core NLP is written in JAVA, JAVA should be installed in your device .it offers the interface for many programming languages including python. The feature includes task like a parser, sentiment analysis, bootstrap pattern learning, Part-of-speech tagging (POS), named entity recognizer (NER), open-source information extraction tool and coreference resolution system . CoreNLP support four human languages apart from English -Arabic, Chinese, German, French, Spanish. With  Core NLP we can extract all text property. Core NLP is great for a beginner, it has an easy interface, versatile, Great for designing the prototype.

8. Genism :

Gensim is a natural language processing python library design for topic modeling,document indexing, and similarity retrieval with large corpora. It can handle large text corpora with the help of efficiency data streaming and incremental algorithms. All algorithms in genism are memory independent concerning corpus size hence it can process input larger than RAM. It has extensive documentation and mostly depends on NumPy and SciPy for Scientific computing, thus you have to install these package first before installing Genism. Genism feature includes such as the efficient multicore implementation of popular algorithms, including online Latent Semantic Analysis, Latent Dirichlet Allocation, Random Projection, Hierarchical Dirichlet process. It is fast and has integration possibilities with NLTK.