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Top2Vec Topic Modelling and Semantic Search Python Demo on arxviv Dataset #NLP
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In this video I demonstrate using a Kaggle notebook how topic modelling and semantic search can be done using Top2Vec python library. Top2Vec is an algorithm for topic modeling and semantic search. It automatically detects topics present in text and generates jointly embedded topic, document and word vectors. Once you train the Top2Vec model you can:
• Get number of detected topics.
• Get topics.
• Get topic sizes.
• Get hierarchichal topics.
• Search topics by keywords.
• Search documents by topic.
• Search documents by keywords.
• Find similar words.
• Find similar documents.
• Expose model with RESTful-Top2Vec
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Relevant Links:
• Get number of detected topics.
• Get topics.
• Get topic sizes.
• Get hierarchichal topics.
• Search topics by keywords.
• Search documents by topic.
• Search documents by keywords.
• Find similar words.
• Find similar documents.
• Expose model with RESTful-Top2Vec
If you like such content please subscribe to the channel here:
Relevant Links:
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Top2Vec Topic Modelling and Semantic Search Python Demo on arxviv Dataset #NLP
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