Comparison of top 6 Python NLP libraries
There are many things about Python that makes it a top programming language for an NLP project. Python’s syntax and semantics are transparent, making it an excellent choice for Natural Language Processing. Moreover, it’s simple and offers excellent support for integration with other languages and tools.
But it also provides developers with extensive libraries that handle many NLP-related tasks such as document classification, topic modeling, part-of-speech (POS) tagging, and sentiment analysis.
Read on to see 6 amazing Python Natural Language Processing libraries that have over the years helped us deliver quality projects to our clients.
1. Natural Language Toolkit (NLTK)
Supporting tasks such as classification, tokenization, stemming, tagging, parsing, and semantic reasoning- this library is your main tool for natural language processing. Today it serves as an educational foundation for Python developers who are dipping their toes in NLP. The library was developed by Steven Bird and Edward Loper at the University of Pennsylvania, and played a key role in breakthrough NLP research. Many universities around the globe now use NLTK in their courses.
2. TextBlob
TextBlob is a must for developers who are starting their journey with NLP in Python and want to make the most of their first encounter with NLTK. It basically provides beginners with an easy interface to help them learn the most basic NLP tasks like sentiment analysis, pos-tagging, or noun phrase extraction.
3. CoreNLP
This library was developed at Stanford University and it’s written in Java. Yet it is equipped with wrappers for many different languages, including Python. Hence, it comes in handy to Python developers interested in building NLP functionalities. The library is really fast and works well in product development environments. Moreover, some of CoreNLP components can be integrated with NLTK which is bound to boost the efficiency of the latter.
4. Gensim
Gensim is a Python library that specializes in identifying semantic similarity between two documents through vector space modeling and topic modeling toolkit. It can handle large text collections with the help of efficiency data streaming and incremental algorithms which is more than we can say about other packages that only target batch and in-memory processing.
5. spaCy
This relatively young library was designed for production usage – that’s why it’s so much more accessible than NLTK. spaCy offers the fastest syntactic parser available on the market today. Moreover, since the toolkit is written in Cython, it’s also really speed. In comparison to the libraries we’ve covered so far, spaCy supports the smallest number of languages (seven). However, its growing popularity means that it might start supporting more of them soon.
6. polyglot
This slightly lesser-known library is one of our favorites because it offers a broad range of analysis and impressive language coverage. Thanks to NumPy, it also works really fast. Using polyglot is similar to spaCy – it’s very straightforward and will be an excellent choice for projects involving a language spaCy doesn’t support. The library stands out from the crowd also because it requests the usage of a dedicated command in the command line through the pipeline mechanisms. Definitely worth a try.
Developing software that can handle natural language can be challenging. But thanks to Python’s extensive toolkit, developers get all the support they need while building amazing tools.
These 6 libraries and Python’s innate characteristics make it a top choice for any NLP project.
Linear Regression
Regression is a technique used to model and analyze the relationships between variables and often times how they contribute and are related to producing a particular outcome together. A linear regression refers to a regression model that is completely made up of linear variables. Beginning with the simple case, Single Variable Linear Regression is a technique used to model the relationship between a single input independent variable (feature variable) and an output dependent variable using a linear model, i.e., a line.
The more general case is Multi Variable Linear Regression where a model is created for the relationship between multiple independent input variables (feature variables) and an output dependent variable. The model remains linear in that the output is a linear combination of the input variables. We can model a multi-variable linear regression as the following:
Y = a_1*X_1 + a_2*X_2 + a_3*X_3 ……. a_n*X_n + b
Where a_n are the coefficients, X_n are the variables and b is the bias. As we can see, this function does not include any non-linearities and so is only suited for modeling linearly separable data. It is quite easy to understand as we are simply weighing the importance of each feature variable X_n using the coefficient weights a_n. We determine these weights a_n and the bias busing a Stochastic Gradient Descent (SGD).