Natural Language Processing (NLP) is a branch of artificial intelligence that helps computers read, interpret, and understand human language. The goal of NLP is for machines to carry out repetitive and high-volume tasks that would otherwise be completed by humans.
Natural Language Processing plays a very important role in structuring big data because it prepares text and speech for machines so that they’re able to interpret, process, and organize information
- Large-scale analysis
- Automate processes in real-time
- Consistent and unbiased criteria
Also known as parsing or syntax analysis ― identifies the syntactic structure of a text and the dependency relationships between words, represented on a diagram called a parse tree.
It involves:
- Tokenization
- Part-of-speech tagging
- Dependency Parsing
- Constituency Parsing
- Lemmatization
- Stemming
- Stopword Removal
- Semantic
- Word Sense Disambiguation
- Relationship Extraction
- Naive Bayes
- Support Vector Machines (SVM)
- Deep Learning
- TF-IDF (term frequency-inverse document frequency)
- Regular Expressions (regex)
- CRF (conditional random fields)
- Rapid Automatic Keyword Extraction (RAKE)
- Chatbots
- Categorizing qualitative feedback
- Automating Processes in Customer Service
- Automatic Summarization
- Machine Translation
- Natural Language Generation
SpaCy is a free open source library for advanced NLP in Python. It has been specifically designed to build NLP applications that can help you understand large volumes of text. That’s one of the differences with its main competitor, NLTK, which was created mostly for research and teaching purposes. SpaCy is fast, easy to use, and very well documented. Instead of presenting you with all the available options to solve an NLP problem, it focuses on the best algorithm you can use for that task. However, for the time being, it only supports the English language.
Neural networks are computational structures they attempt to mirror the way human brain recognizes patterns
https://www.coursera.org/learn/classification-vector-spaces-in-nlp/home/welcome