How to Do Extract Keywords from a Sentence using KeyBERT?
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Condition for Extract Keywords from a Sentence using KeyBERT
Description: The primary goal is to determine the "best key root node" by identifying the most relevant keyword or phrase from the text. The term "root node" here implies the central idea or the most representative keyword in the given sentence.
Step-by-Step Process
Initialize the KeyBERT Model: This initializes the KeyBERT model. Under the hood, KeyBERT leverages pre-trained transformer models (like BERT) to generate embeddings for the input text and extract meaningful keywords or key phrases. Key Features: It ranks keywords based on their semantic similarity to the entire sentence.By default, it uses the BERT model for generating embeddings.
Input Sentence: This is the text from which you want to extract keywords. The sentence represents a topic or concept you want to analyze.
Extract Keywords: sentence: The input text for analysis. keyphrase_ngram_range=(1, 1): Specifies the size of the keywords to extract. Here,(1, 1) ensures only single words (unigrams) are considered. stop_words='english': Removes common words (like "is," "the," "of," etc.) that dont contribute much meaning.
Find the Best Key Root Node: The max() function selects the keyword with the highest similarity score. The key=lambda x: x[1] tells Python to sort based on the second element of each tuple,which represents the score.
Output: The keyword with the highest relevance score is identified as the best key root node.
Sample Code
from keybert import KeyBERT
# Initialize KeyBERT model
kw_model = KeyBERT()
# Your input sentence
sentence = "Machine learning is a method of data analysis that automates analytical model building."
# Extract keywords/phrases
keywords = kw_model.extract_keywords(sentence, keyphrase_ngram_range=(1, 1), stop_words='english')
# Display keywords
print("Extracted Keywords:", keywords)
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