ext Preprocessing in NLP
Before feeding data into an NLP model, it must be preprocessed to remove noise and standardize the input. The preprocessing pipeline typically involves:
Removing Special Characters and Stopwords: Eliminating unnecessary punctuation, numbers, and common words like "the" or "and" that do not contribute significantly to meaning.
Lowercasing: Converting all text to lowercase to maintain consistency.
Tokenization: Splitting text into words, subwords, or sentences for further processing.
Lemmatization/Stemming: Normalizing words to their base forms to reduce redundancy in vocabulary.
Vectorization: Converting text into numerical representations using techniques like TF-IDF, Word2Vec, or Transformer embeddings.
Proper preprocessing ensures that the NLP model receives structured and meaningful input, improving its performance and accuracy.