Semanlink - Semantic Web ; Favoris AND Semantic Search

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Arxiv Doc - BERT - Cannibalizing LLMs - Documentation - ElasticSearch - GitHub project - Good - Howto - LLM - NLP - NLP: use cases - Raphaël Sourty - Retriever - SearchMonkey - Semantic Search - Sentence Embeddings - Similarity queries / Vector search - Stack Overflow - Text Embeddings - Top-k - Tweet - Vector database - Wikipedia -

Semantic Web ; Favoris AND Semantic Search

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20 Documents

Ask like a human: Implementing semantic search on Stack Overflow - Stack Overflow Blog

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2023-08-11 A propos

[2305.12517] Retrieving Texts based on Abstract Descriptions

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2023-06-15 A propos

@yoavgo sur Twitter : "searching by description can be very useful, but current embedding models will give you texts that are *similar* to the description, rather than texts that *adhere to it*..."

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2023-05-24 A propos

Jerry Liu sur Twitter : "Tuning top-k for semantic search is challenging... Introducing the LlamaIndex AutoRetriever for vector databases"

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2023-05-13 A propos

Alex sur Twitter : "Semantic search: how you can leverage both @CohereAI and @pinecone libraries to quickly build a POC..."

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2022-11-08 A propos

Understanding Semantic Search and Question Answering | deepset

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2022-06-03 A propos

[2008.09470] Top2Vec: Distributed Representations of Topics

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2022-04-28 A propos

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

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

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2022-01-29 A propos

Semantic Search — Sentence-Transformers documentation

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2022-01-29 A propos

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

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2021-12-05 A propos

raphaelsty/RetrieverReader: Fast API QA

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2021-10-04 A propos

Building a sentence embedding index with fastText and BM25 | by David Mezzetti | Towards Data Science

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2021-09-30 A propos

neuml/txtai: Build AI-powered semantic search applications

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txtai

2021-09-30 A propos

Semantic Search with S-BERT is all you need

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2021-06-05 A propos

[2007.12603] IR-BERT: Leveraging BERT for Semantic Search in Background Linking for News Articles

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2021-04-12 A propos

[1706.00957] Semantic Vector Encoding and Similarity Search Using Fulltext Search Engines

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2017-11-11 A propos

Semantic search with NLP and elasticsearch - Stack Overflow

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2017-04-28 A propos

Studio Ousia Envisions A World Of Semantic Augmented Reality - Semanticweb.com

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2014-03-26 A propos

Semantic Search Arrives at the Web

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2008-07-19 A propos