Illustrated Guide to NLP Tokenization

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Natural Language Processing (NLP) tokenization is a fundamental preprocessing step that transforms raw text into meaningful tokens, facilitating the downstream pipeline for text analytics and machine learning models. This process involves sophisticated techniques like word tokenization, sentence segmentation, subword tokenization, and byte-pair encoding, each optimizing for diverse linguistic features. Advanced algorithms such as regex-based tokenizers, rule-based tokenizers, and neural tokenizers ensure the handling of edge cases, including punctuation, compound words, and multilingual corpora. The integration of tokenization with embedding layers, such as Word2Vec, GloVe, and contextual embeddings like BERT and GPT, enhances semantic representation. Tokenization's efficacy in transforming unstructured data into structured data underpins tasks like part-of-speech tagging, named entity recognition, and syntactic parsing, ultimately driving the performance of transformer-based architectures and language models in NLP.

[@GPTandChill]

Small Clarification: Technically the question mark comes before alphabet characters in ASCII, but the idea presented in the video holds regardless.

[@avi12]

5:44 I was wondering why you didn't do
for word, i in sorted_list:
mapping[word] = i + 1

[@ShahidulAbir]

I think you meant
for i, word in enumerate(sorted_list):
mapping[word] = i + 1

[@GPTandChill]

Wanted to keep the code as readable as possible for those without Python background!