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The Main Approaches to Natural Language Processing Tasks | NLP foundation - Part 2
3 месяца назад
Approaches to NLP
Heuristics-Based NLP
Machine Learning for NLP
Deep Learning for NLP
Lexicon-based sentiment analysis
Rule-based NLP
regex
Wordnet
Naive Bayes
Support Vector Machine
SVM
Hidden Markov Model
HMM
Conditional Random Field
CRF
Bayes' theorem
classification
Face detection
image classification
text categorization
speech-to-text converter
Speech recognition system
Part-of-Speech (POS) Tagging
RNN
LSTM
CNN
Transformers
Autoencoders
Alexa
Siri
NLP
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The Main Approaches to Natural Language Processing Tasks | NLP foundation - Part 2
With the rise of artificial intelligence, automation is becoming a part of everyday life. Natural Language Processing (NLP) has proven to be a key part of this breakthrough. Natural Language Processing bridges the gap between computers, AI, and computational linguistics. Learn more about NLP approaches this guide to statistical and symbolic NLP. A simple sentence spoken by humans consists of different tones, words, meanings, and values. Expert AI systems are able to leverage these hidden structures and meanings to understand human behaviour. However, we often need a very close and detailed assessment to conclude what meaning might and might not be correct. When we have a large amount of text data, it can become impossible to read quickly. Raw text data in English or other languages is an example of unstructured data. This kind of data does not fit into a relational database and is hard to interpret with computer programs. Natural language processing is a sub-field of AI which deals with how computers interpret, comprehend, and manipulate human language. NLP is much more than speech and text analysis. Depending on what needs to be done, there can be different approaches
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