Semanlink - MaartenGr/BERTopic: Leveraging BERT and c-TF-IDF to create easily interpretable topics.

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sl:bookmarkOf : https://github.com/MaartenGr/BERTopic
sl:creationDate : 2022-03-10
sl:creationTime : 2022-03-10T09:41:50Z
sl:relatedDoc : http://www.semanlink.net/doc/2022/03/ddangelov_top2vec_top2vec_lear

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Bookmark of: https://github.com/MaartenGr/BERTopic

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ddangelov/Top2Vec: Top2Vec learns jointly embedded topic, document and word vectors.

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> 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.
>
> "Update: Pre-trained Universal Sentence Encoders and BERT Sentence Transformer now available for embedding."

> **The assumption the algorithm makes is that many semantically similar documents are indicative of an underlying topic**. The first step is to create a joint embedding of document and word vectors. Once documents and words are embedded in a vector space the goal of the algorithm is to find dense clusters of documents, then identify which words attracted those documents together. Each dense area is a topic and the words that attracted the documents to the dense area are the topic words.

> Once you train the Top2Vec model you can:
> - ...
> - Get **hierarchical topics**.
> - Search topics by keywords.
> - Search documents by topic, by keywords.
> - Find similar words, similar documents.

Refered by [BERTopic](doc:2022/03/maartengr_bertopic_leveraging_)

2022-03-10 A propos

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