This program is an example of feature extraction with text. It predicts whether a text message is SPAM or a legitimate text message.
This program looks at surrounding text to determine a given word's part of speech. It considers a language's full vocabulary to apply a morphological analysis to words.
This program is an example of rule based matching. It demonstrates how you can find words and phrases inside text.
Text classifcation project - We'll be able to tell whether a movie review is positive or negative based off the texts.
Named-entity recognition(NER) locates and classifes named entity mentions in unstructured text into pre-defined categories such as the person names, organizations, locations, etc.
Simple language processing examples that shows classification techniques with the naive Bayes algorithm.
The example below shows how spacy can note the difference in present and past tense based on the context of the sentence.
Spacy intro - introduction to Spacy along with a visualization.
This is an example of Porter's Alogrithm and Snowball-both stemming tools created by Martin Porter.
This is an example of stop words used in Spacy.