spaCy Archives - Machine Learning Plus

Complete Guide to Natural Language Processing (NLP) – with Practical Examples

Natural language processing (NLP) is the technique by which computers understand the human language. NLP allows you to perform a wide range of tasks such as classification, summarization, text-generation, translation and more. NLP has advanced so much in recent times that AI can write its own movie scripts, create poetry, summarize text and answer questions …

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Building chatbot with Rasa and spaCy

SpaCy Text Classification – How to Train Text Classification Model in spaCy (Solved Example)?

2 Comments / NLP / By Shrivarsheni

Text Classification is the process categorizing texts into different groups. SpaCy makes custom text classification structured and convenient through the textcat component. Text classification is often used in situations like segregating movie reviews, hotel reviews, news data, primary topic of the text, classifying customer support emails based on complaint type etc. For many real-life cases, …

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spaCy Tutorial – Complete Writeup

spaCy is an advanced modern library for Natural Language Processing developed by Matthew Honnibal and Ines Montani. This tutorial is a complete guide to learn how to use spaCy for various tasks. Overview 1. Introduction The Doc object 2. Tokenization with spaCy 3. Text-Preprocessing with spaCy 4. Lemmatization 5. Strings to Hashes 6. Lexical attributes …

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101 NLP Exercises (using modern libraries)

2 Comments / NLP / By Shrivarsheni

I hope you found this useful. For more such posts, stay tuned to our page ! Desired Output: #> [(‘incredible’, 0.90), #> (‘awesome’, 0.82), #> (‘unbelievable’, 0.82), #> (‘fantastic’, 0.77), #> (‘phenomenal’, 0.76), #> (‘astounding’, 0.73), #> (‘wonderful’, 0.72), #> (‘unbelieveable’, 0.71), #> (‘remarkable’, 0.70), #> (‘marvelous’, 0.70)] Difficulty Level : L2 22. How to …

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Training Custom NER models in SpaCy to auto-detect named entities [Complete Guide]

15 Comments / NLP / By Shrivarsheni

Named-entity recognition (NER) is the process of automatically identifying the entities discussed in a text and classifying them into pre-defined categories. Categories could be entities like ‘person’, ‘organization’, ‘location’ and so on. The spaCy library allows you to train NER models by both updating an existing spacy model to suit the specific context of your …

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Lemmatization Approaches with Examples in Python

10 Comments / NLP / By Selva Prabhakaran

Lemmatization is the process of converting a word to its base form. The difference between stemming and lemmatization is, lemmatization considers the context and converts the word to its meaningful base form, whereas stemming just removes the last few characters, often leading to incorrect meanings and spelling errors. Comparing Lemmatization Approaches in Python. Photo by …

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