Natural Language Processing Usecases with Python

1. Master Natural Language Processing in 2022 with Best Resources

As already mentioned earlier, Deep Learning is a subdomain of machine learning. It is far more generalized as it comes up with generalized predictions compared to traditional machine learning due to the introduction of Artificial Neural Networks or ANN. Practicing NLP with Deep Learning is an essential step to making a career in AI and Data Science. Nowadays, almost every real-world AI application is built on top of Deep Learning (Neural-Net) architectures. It gives highly generalized performance and fantastic accuracy on real-world data.

Categories: Career, NLP

Level: Intermediate

Link to the entire article: https://www.analyticsvidhya.com/blog/2022/01/master-natural-language-processing-in-2022-with-best-resources/

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2. Pattern Library for Natural Language Processing in Python

There is a wide variety of data available on the internet. Data can be numbers, images, text, audio, and son. The vast amount of data available online and generated is vast. The vast amount of text data can be overwhelming to analyze and understand.

Categories: Libraries, NLP, Python

Level: Advanced

Link to the entire article: https://www.analyticsvidhya.com/blog/2021/11/pattern-library-for-natural-language-processing-in-python/

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3. An Introduction to Stemming in Natural Language Processing

Julie Beth Lovins wrote the first published stemmer in 1968. This article was groundbreaking in its day and had a significant effect on subsequent efforts in this field. Her paper makes reference to three previous major attempts at stemming algorithms: one by Professor John W. Tukey of Princeton University, another by Michael Lesk of Harvard University under the direction of Professor Gerard Salton, and a third algorithm developed by James L. Dolby of R and D Consultants in Los Altos, California.

Categories: NLP, Python, Text

Level: Beginner

Link to the entire article: https://www.analyticsvidhya.com/blog/2021/11/an-introduction-to-stemming-in-natural-language-processing/

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4. Building Language Models in NLP

A language model in NLP is a probabilistic statistical model that determines the probability of a given sequence of words occurring in a sentence based on the previous words. It helps to predict which word is more likely to appear next in the sentence. Hence it is widely used in predictive text input systems, speech recognition, machine translation, spelling correction, etc. The input to a language model is usually a training set of example sentences.

Categories: Model Deployment, NLP, Project

Level: Beginner

Link to the entire article: https://www.analyticsvidhya.com/blog/2022/01/building-language-models-in-nlp/

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5. Introduction to Natural Language Processing and Tokenization

Keras is an API designed not for machines but for human beings. Keras reduces cognitive load by offering consistent and simple APIs. It also reduces the number of actions required by users for a common use case. The documentation provided by Keras is detailed and extensive helping developers to easily take advantage. It is the most used deep learning library also used by NASA, CERN, and many more organizations around the world.

Categories: NLP, python

Level: Advanced

Link to the entire article: https://www.analyticsvidhya.com/blog/2022/01/introduction-to-natural-language-processing-and-tokenization/

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6. A Guide to Automated Deep/Machine Learning for Natural Language Processing: Text Prediction

This article starts by discussing the fundamentals of Natural Language Processing (NLP) and later demonstrates using Automated Machine Learning (AutoML) to build models to predict the sentiment of text data. Other applications of NLP are translation, speech recognition, chatbot, etc. You may be thinking that this article is general because there are many NLP tutorials and sentiment analyses on the internet. But, this article tries to show something different.

Categories: Deep Learning, Guide, Machine Learning, NLP, Programming, Python

Level: Advanced

Link to the entire article: https://www.analyticsvidhya.com/blog/2021/11/a-guide-to-automated-deep-machine-learning-for-natural-language-processing-text-prediction/

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7. Rapid Keyword Extraction (RAKE) Algorithm in Natural Language Processing

In the field of Machine Learning, thanks to ‘No Free Lunch” (NFL), we have multiple options of algorithms to solve a problem. Is it a boon? Unfortunately, it is not. You can not run for the entire buffet menu. While I was working on a project based on NLP, this was precise, what had happened to me.

Categories: Algorithm, NLP, Text

Level: Advanced

Link to the entire article: https://www.analyticsvidhya.com/blog/2021/10/rapid-keyword-extraction-rake-algorithm-in-natural-language-processing/

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8. Cleaning and Pre-processing textual data with NeatText library

Unstructured text data can be a problem while solving NLP problems. There is a need to pre-process any unstructured text data in order for us to build an effective NLP model. Hence pre-processing textual data is an important step while building any NLP model. Converting text into numbers is important as the machine learning models take only numbers as inputs. Therefore converting string objects(text) into ‘int’ objects is necessary. There are many ways to pre-process text. One way is to hard code every step and processes the text data through that code. Another way is to use any Natural Language Processing package that does the work for us using simple commands. One such package is NeatText.

Categories: Libraries, NLP, Python, Text

Level: Beginner

Link to the entire article: https://www.analyticsvidhya.com/blog/2021/10/cleaning-and-pre-processing-textual-data-with-neattext-library/

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9. Text Preprocessing in Python – Getting started with NLP

NLP or Natural Language Processing is the science of processing, understanding, and generating human language by machines. Using NLP, information can be extracted from unstructured data, trained to generate responses for human queries, classify text into appropriate categories. News articles, social media posts, and online reviews are some of the publicly available sources that are rich in information. NLP is used to derive meaningful insights from these sources but training NLP algorithms directly on the text, in its free form, can induce a lot of noise and add unnecessary complexity.

Categories: Data Science, NLP, Python, Text

Level: Beginner

Link to the entire article: https://www.analyticsvidhya.com/blog/2021/08/text-preprocessing-in-python-getting-started-with-nlp/

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