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Natural Language Processing(NLP), a field of AI, aims to understand the semantics and connotations of natural human languages. It focuses on extracting meaningful information from text and train data models based on the acquired insights. The primary NLP functions include text mining, text classification, text analysis, sentiment analysis, word sequencing, speech recognition & generation, machine translation, and dialog systems, to name a few.
Thanks to the development of useful Python NLP Libraries, today, NLP is finding applications across the various parallels of the industrial landscape. In fact, NLP has now become an integral part of Deep Learning development. Extracting valuable information from free text is essential for developing chatbots, patent research & analysis, voice/speech recognition, patient data processing, and querying image content, among other use cases of NLP.
The fundamental aim of Python NLP Libraries is to simplify text preprocessing. A good NLP library should be able to correctly convert free text sentences into structured features (for example, cost per hour) that can easily be fed into ML or DL pipelines. Also, an NLP library should have a simple-to-learn API, and it must be able to implement the latest and greatest algorithms and models efficiently.
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Although there are numerous NLP libraries designed for specific NLP applications, today, we’re going to draw a comparison of the functions of the top NLP libraries in Python.
Now, let’s dive into the discussion about the top Python NLP libraries!
What is an NLP Library?
An NLP (Natural Language Processing) library is a software package or framework specifically designed to facilitate the building, implementation, and use of natural language processing tasks and applications. These Python NLP libraries often include a wide range of tools and resources for handling and interpreting human language. Text tokenization, sentiment analysis, machine translation, named entity recognition, part-of-speech tagging, and other applications can all benefit from these.
Python NLP libraries frequently contain pre-trained models and algorithms that are ready to use right out of the box or that have been adjusted for certain tasks or domains. When working on different NLP applications in domains like information retrieval, text mining, chatbots, sentiment analysis, language translation, and more, researchers, data scientists, developers, and engineers frequently employ them.
Key Components of an Python NLP Library:
- Text Processing Tools
- Statistical and Machine Learning Models
- Language Resources
- Evaluation Metrics and Tools
- Integration with Deep Learning Frameworks
Top Python NLP Libraries
1. Natural Language Toolkit (NLTK)
One of the NLTK is one of the leading platforms for building Python programs that can work with human language data. It presents a practical introduction to programming for language processing. NLTK comes with a host of text processing libraries for sentence detection, tokenization, lemmatization, stemming, parsing, chunking, and POS tagging.
NLTK provides easy-to-use interfaces to over 50 corpora and lexical resources. The tool has the essential functionalities required for almost all kinds of natural language processing tasks with Python.
2. Gensim
Gensim is a Python library designed specifically for “topic modeling, document indexing, and similarity retrieval with large corpora.” All algorithms in Gensim are memory-independent, w.r.t., the corpus size, and hence, it can process input larger than RAM. With intuitive interfaces, Gensim allows for efficient multicore implementations of popular algorithms, including online Latent Semantic Analysis (LSA/LSI/SVD), Latent Dirichlet Allocation (LDA), Random Projections (RP), Hierarchical Dirichlet Process (HDP) or word2vec deep learning.
Gensim features extensive documentation and Jupyter Notebook tutorials. It largely depends on NumPy and SciPy for scientific computing. Thus, you must install these two Python packages before installing Gensim.
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3. CoreNLP
Stanford CoreNLP comprises of an assortment of human language technology tools. It aims to make the application of linguistic analysis tools to a piece of text easy and efficient. With CoreNLP, you can extract all kinds of text properties (like named-entity recognition, part-of-speech tagging, etc.) in only a few lines of code.
Since CoreNLP is written in Java, it demands that Java be installed on your device. However, it does offer programming interfaces for many popular programming languages, including Python. The tool incorporates numerous Stanford’s NLP tools like the parser, sentiment analysis, bootstrapped pattern learning, part-of-speech (POS) tagger, named entity recognizer (NER), and coreference resolution system, to name a few. Furthermore, CoreNLP supports four languages apart from English – Arabic, Chinese, German, French, and Spanish.
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4. spaCy
spaCy is an open-source NLP library in Python. It is designed explicitly for production usage – it lets you develop applications that process and understand huge volumes of text.
spaCy can preprocess text for Deep Learning. It can be be used to build natural language understanding systems or information extraction systems. spaCy is equipped with pre-trained statistical models and word vectors. It can support tokenization for over 49 languages. spaCy boasts of state-of-the-art speed, parsing, named entity recognition, convolutional neural network models for tagging, and deep learning integration.
5. TextBlob
TextBlob is a Python (2 & 3) library designed for processing textual data. It focuses on providing access to common text-processing operations through familiar interfaces. TextBlob objects can be treated as Python strings that are trained in Natural Language Processing.
TextBlob offers a neat API for performing common NLP tasks like part-of-speech tagging, noun phrase extraction, sentiment analysis, classification, language translation, word inflection, parsing, n-grams, and WordNet integration.
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6. Pattern
Pattern is a text processing, web mining, natural language processing, machine learning, and network analysis tool for Python. It comes with a host of tools for data mining (Google, Twitter, Wikipedia API, a web crawler, and an HTML DOM parser), NLP (part-of-speech taggers, n-gram search, sentiment analysis, WordNet), ML (vector space model, clustering, SVM), and network analysis by graph centrality and visualization.
Pattern can be a powerful tool both for a scientific and a non-scientific audience. It has a simple and straightforward syntax – the function names and parameters are chosen in a way so that the commands are self-explanatory. While Pattern is a highly valuable learning environment for students, it serves as a rapid development framework for web developers.
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7. PyNLPl
Pronounced as ‘pineapple,’ PyNLPl is a Python library for Natural Language Processing. It contains a collection of custom-made Python modules for Natural Language Processing tasks. One of the most notable features of PyNLPl is that it features an extensive library for working with FoLiA XML (Format for Linguistic Annotation).
PyNLPl is segregated into different modules and packages, each useful for both standard and advanced NLP tasks. While you can use PyNLPl for basic NLP tasks like extraction of n-grams and frequency lists, and to build a simple language model, it also has more complex data types and algorithms for advanced NLP tasks.
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8. Polyglot
Multilingual natural language processing (NLP) software, Polyglot, is an open-source program that supports more than 130 languages. Created by the MIT Computer Science and Artificial Intelligence Laboratory, the NLP toolkit Polyglot offers a variety of operations including named entity recognition, sentiment analysis, tokenization, and part-of-speech tagging. Due to its use of pre-trained models and embeddings to provide robust linguistic analysis across different languages, it is extremely useful for academics and developers working with multilingual datasets. Polyglot is the best option for tasks like sentiment analysis in social media data, cross-lingual information retrieval, and language recognition because of its intuitive interface and extensive linguistic coverage. You may be sure that it will constantly be upgraded and able to adapt to new linguistic issues because of its active community and ongoing development.
9. scikit-learn
Scikit-learn, a collection of many tools and methods, is the cornerstone of the Python machine-learning ecosystem. The productivity of data analysis and model construction is enhanced by this cooperatively built open-source toolkit, which has an intuitive API. Among the many diverse machine learning tasks that scikit-learn supports are model evaluation, dimensionality reduction, regression, clustering, and classification. With its well-documented interface and plethora of tutorials that accommodate users of all skill levels, it empowers both inexperienced and seasoned users accordingly.
One resource that is widely utilized in both academia and industry is scikit-learn. Both straightforward data exploration tasks and complex machine learning pipelines and predictive models depend on it. It is a vital tool for solving a variety of real-world issues in numerous industries due to its adaptability, scalability, and resilience.
10. Hugging Face Transformer
Hugging Face Transformers, a freshly published package for natural language processing, is built on top of the PyTorch and TensorFlow frameworks. Several transformer-based models, such as BERT, GPT, RoBERTa, and others, are accessible through this library, which was developed by the Hugging Face team.
The user-friendly interface and large model center of pre-trained models make them easily accessible for tasks like text categorization, question answering, language synthesis, and translation. Hugging Face Transformers provide rapid testing and deployment in various academic and industrial situations by streamlining the optimization and deployment of transformer models. Professionals in business and NLP enthusiasts looking for cutting-edge solutions always turn to this product because of its seamless integration and robust features.
Conclusion
After getting a detailed account of the functionalities of the different Python NLP libraries, we can see that although most of them can perform similar NLP tasks, each library has unique features/approaches for specific NLP applications. Mainly, the usage of these Python NLP libraries depends on the NLP problem at hand.
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Python has an active community that most developers create libraries for their own purposes and later release it to the public for their benefit. Here are some of the common machine learning libraries used by Python developers. If you want to update your data science skills, check out IIIT-B’s Executive PG Programme in Data Science program.
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