Open Source Frontline (ID: OpenSourceTop) compiled by Yuan Mei
Compiled from: https://www.kdnuggets.com/2018/07/comparison-top-6-python-nlp-libraries.html
Nowadays, Natural Language Processing (NLP) has become increasingly popular, especially in the context of deep learning. NLP is one of the fields of artificial intelligence that aims to understand and extract important information from text, and to further train based on text data. The main tasks of NLP include speech recognition and generation, text analysis, sentiment analysis, machine translation, and more.
In the past few decades, only experts with certain language education could work in the field of Natural Language Processing. Besides mathematics and machine learning, they should also be familiar with some key language concepts. Now, we can use pre-written NLP libraries. Their main purpose is to simplify text preprocessing, allowing us to focus on building machine learning models and hyperparameter tuning.
There are many tools and libraries that can address NLP issues. Today, we will summarize our experience by comparing 6 popular NLP libraries.
Overview
NLTK (Natural Language Toolkit) is used for tasks such as tokenization, lemmatization, stemming, parsing, and POS tagging. This library has tools available for almost all NLP tasks.
● spaCy is the main competitor of NLTK. Both libraries can be used for the same tasks.
● scikit-learn provides a large library for machine learning and also offers tools for text preprocessing.
● gensim is a specialized Python toolkit for topic modeling and vector space modeling.
● The Pattern library serves as a web mining module, thus supporting NLP tasks.
● polyglot is another Python package for NLP. It is not very popular but can also be used for various NLP tasks.
Below are some pros and cons of these open source libraries:
Pros of NLTK:
● The most well-known and comprehensive NLP library.
● Many third-party extensions.
● Many methods for each NLP task.
● Fast sentence tokenization.
● Supports the most languages compared to other libraries.
Cons:
● Complex to learn and use.
● Very slow.
● In syntactic tokenization, NLTK only splits sentences without analyzing semantic structures.
● Processes strings, which is not typical for object-oriented language Python.
● Does not provide neural network models.
● Does not integrate word vectors.
Pros of spaCy:
● The fastest NLP framework.
● Easy to learn and use, as it has highly optimized tools for each task.
● More object-oriented compared to other libraries.
● Uses neural networks to train some models.
● Provides built-in word vectors.
● Actively supported and developed.
Cons:
● Lacks flexibility compared to NLTK’s sentence tokenization and is slower than NLTK.
● Does not support multiple languages.
● Only suitable for 7 languages and a ‘multilingual’ model.
Conclusion:
In this article, we compared some features of several popular NLP libraries. Although most of their functionalities overlap, they also have unique methods for addressing specific problems. Currently, the most popular NLP packages are NLTK and spaCy. They are the main competitors in the NLP field. In our view, the differences between them lie in their general philosophy for solving problems.
NLTK is more academic. You can use it to try different methods and algorithms, combine them, and so on. In contrast, spaCy provides an out-of-the-box solution for each problem. You don’t have to consider which method is better: the authors of spaCy have already solved that problem. Additionally, spaCy is very fast (several times faster than NLTK), but a downside is that the number of languages supported by spaCy is limited, although the number of supported languages has been steadily increasing. Therefore, we believe spaCy is the best choice in most cases, but if you want to try something special, you can use NLTK.
Although both libraries are very popular, there are still many other different options available. The choice of which NLP package to use depends on the type of problem you want to solve.
