Top 7 Python NLP Libraries
Hi all,
Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. Python’s simple, easy to learn syntax emphasizes readability and therefore reduces the cost of program maintenance. Python libraries ease the need of writing codes from the very beginning. Here are the top NLP Libraries you should be aware of.
1. NLTK
NLTK is a leading platform for building Python programs to work with human language data. NLTK is available for Windows, Mac OS X, and Linux. Best of all, NLTK is a free, open source, community-driven project. It has text processing libraries for classification, tokenization, stemming, pos tagging, parsing, and semantic reasoning. NLTK is a Python package that you can use for NLP.
2. SpaCy
spaCy is a free, open-source library for advanced Natural Language Processing (NLP) in Python. It will be used to build information extraction, natural language understanding systems, and to pre-process text for deep learning. Spacy is used in NLP projects, such as Tokenization, Lemmatisation, Part-of-speech(POS) tagging, Entity recognition, Dependency parsing, Sentence recognition, Word-to-vector transformations, and other cleaning and normalization text methods.
3. Gensim
Gensim is a free open-source Python library for representing documents as semantic vectors, as efficiently (computer-wise) and painlessly (human-wise) as possible. Gensim is implemented in Python and Cython for performance. Gensim is designed to handle large text collections using data streaming and incremental online algorithms, which differentiates it from most other machine learning software packages that target only in-memory processing. Gensim depends on scipy and numpy. You must have them installed prior to installing gensim.
4. CoreNLP
Stanford CoreNLP provides a set of natural language analysis tools that can give the base forms of words, their parts of speech, whether they are names of companies, people, etc. CoreNLP enables users to derive linguistic annotations for text, including token and sentence boundaries, parts of speech, named entities, numeric and time values, dependency and constituency parses, coreference, sentiment, quote attributions, and relations. People use CoreNLP while writing their own code in Javascript, Python, or some other language. The full Stanford CoreNLP is licensed under the GNU General Public License v3 or later.
5. PyNLPI
PyNLPl is a Python library for Natural Language Processing that contains various modules useful for common, and less common, NLP tasks. PyNLPl can be used for basic tasks such as the extraction of n-grams and frequency lists, and to build simple language model. . PyNLPI can read and process GIZA, Moses++, SoNaR, Taggerdata, and TiMBL data formats, and devotes an entire module to working with FoLiA, the XML document format used to annotate language resources like corpora.
6.TextBlob
TextBlob is a python library and offers a simple API to access its methods and perform basic NLP tasks. With TextBlob, you spend less time struggling with the intricacies of Pattern and NLTK and more time getting results. TextBlob smooths the way by leveraging native Python objects and syntax. TextBlob, which is built on the shoulders of NLTK and Pattern. A big advantage of this is, it is easy to learn and offers a lot of features like sentiment analysis, pos-tagging, noun phrase extraction, etc.
7. Pattern
Pattern is an open-source python library and performs different NLP tasks. Pattern Library is used for NLP by performing tasks such as tokenization, stemming and sentiment analysis. We will also see how the Pattern library can be used for web mining. Pattern comes with built-ins for scraping a number of popular web services and sources (Google, Wikipedia, Twitter, Facebook, generic RSS, etc.), all of which are available as Python modules. Pattern exposes some of its lower-level functionality, allowing you to to use NLP functions, n-gram search, vectors, and graphs directly if you like.
Thank You!
