How can you apply lemmatization with WordNetLemmatizer in NLTK for preprocessing generative AI data

Question

With the code or any other source, can you apply lemmatization with WordNetLemmatizer in NLTK to preprocess generative AI data?

Answer 1

To apply lemmatization using WordNetLemmatizer in NLTK for preprocessing generative AI data, you can refer to the following steps:

• Tokenize the Text: Split the text into individual tokens (words).
• Lemmatize: Use WordNetLemmatizer to convert words into their base forms (lemmas).
• Use POS Tags: Optionally, provide part-of-speech (POS) tags to improve lemmatization accuracy.

Here is the code reference you can refer to:

In the above code, we are using the following:

• Tokenization: The text is split into words using word_tokenize.
• POS Tagging: nltk.pos_tag is used to get part-of-speech tags for each word, which help in determining the correct lemma.
• Lemmatization: The WordNetLemmatizer is used to convert each word into its base form, considering its part-of-speech tag.

Hence, this preprocessing step is useful for generative AI tasks like text generation, as it ensures words are reduced to their root forms, improving consistency and model efficiency.