Semanlink - Machine learning ; Semantic Web AND Semantic Search

> 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 About

How to Build a Semantic Search Engine With Transformers and Faiss | by Kostas Stathoulopoulos | Towards Data Science

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2022-01-29 About

Semantic Search — Sentence-Transformers documentation

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2022-01-29 About

Semantic search through a vectorized Wikipedia (SentenceBERT) with the Weaviate vector search engine

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