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Classical nlp related coding questions

👩‍💻 Technical question

Asked almost 2 years ago in Python by khedidja 
give me methodes of NLP classical and with deep learning

🤖 SheCodes Athena says:

Answered in 6.05 seconds

There are several techniques and methods used in Natural Language Processing (NLP). Some of the most commonly used methods are as follows:

  1. Classical NLP methods:

    • Tokenization: It refers to the process of splitting a text into separate words or tokens.
    • Stop Word Removal: It involves elimination of commonly occurring words such as "the", "an", "a", "in" etc. which do not contribute much to the meaning of the text.
    • Stemming: It involves reducing words to their base or root form. For example, "running", "runs" and "run" will be reduced to "run".
    • Lemmatization: It is similar to stemming but it involves reducing words to their dictionary form. For example, the lemma of "running", "runs" and "run" is "run".
    • Part of Speech (POS) Tagging: It involves assigning a part of speech to each word in a text. For example, whether a word is a noun, verb, adjective or adverb.

  2. Deep Learning methods:

    • Recurrent Neural Networks (RNN): It is a type of neural network which is used for processing sequential data such as text. RNNs can be used for tasks such as language modelling, sentiment analysis, and machine translation.
    • Convolutional Neural Networks (CNN): It is a type of neural network which is used for image processing. However, it can also be used for text classification tasks such as sentiment analysis.
    • Transformer models: It is a deep learning architecture that is used for a variety of NLP tasks including language translation, question answering, and text classification. The most popular of these is the BERT model which uses a transformer architecture.

Here's an example code in Python demonstrating tokenization using the NLTK library:

python
 
 
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import nltk
nltk.download('punkt')
from nltk.tokenize import word_tokenize

text = "Natural Language Processing is a subfield of computer science that deals with the interaction between computers and humans."

tokens = word_tokenize(text)
print(tokens)

Output:

code
 
 
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['Natural', 'Language', 'Processing', 'is', 'a', 'subfield', 'of', 'computer', 'science', 'that', 'deals', 'with', 'the', 'interaction', 'between', 'computers', 'and', 'humans', '.']
NLP classical NLP deep learning RNN CNN transformer models NLTK tokenization
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