Semanlink - Artificial Intelligence ; Search AND Web of data

> 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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2021-12-05 About

raphaelsty/RetrieverReader: Fast API QA

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2021-10-04 About

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

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2021-09-30 About

neuml/txtai: Build AI-powered semantic search applications

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txtai

2021-09-30 About

Semantic Search with S-BERT is all you need

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2021-06-05 About

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

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2021-04-12 About

Supercharging Elasticsearch for extended Knowledge Graph use cases: Data science + business analytics training: Strata Data Conference

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2019-05-16 About

How To Create Natural Language Semantic Search For Arbitrary Objects With Deep Learning

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2018-06-02 About

ML-knn: A lazy learning approach to multi-label learning (2007)

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a lazy learning approach derived from the traditional [k-nearest neighbors algorithm](tag:k_nearest_neighbors_algorithm)

> for each unseen instance, its K nearest neighbors in the training set are firstly identified. After that, based on statistical information gained from the label sets of these neighboring instances, i.e. the number of neighboring instances belonging to each possible class, [Maximum a posteriori (MAP)](tag:maximum_a_posteriori_estimation) principle is utilized to determine the label set for the unseen instance.

Implemented in [scikit-multilearn](http://scikit.ml/api/skmultilearn.adapt.mlknn.html), in [java](https://github.com/lefman/mulan-extended/blob/master/mulan/src/mulan/classifier/lazy/MLkNN.java)

> the first lazy approach proposed specifically for multi-label classification. This is also a binary relevance approach which considers each label independently as a binary classification problem. Instead of a standard k-NN method, however, MLkNN uses the maximum a-posteriori (MAP) (Kelleher et al., 2015) approach combined with k-NN. [src](https://pdfs.semanticscholar.org/af9b/33da37d290c063cd826ab5923d96892a9767.pdf)

2018-03-18 About

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

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