Semanlink - Machine learning ; Semantic Web AND Information retrieval

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Machine learning ; Semantic Web AND Information retrieval

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txtai

17 Documents

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

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2023-08-11 About

[2305.12517] Retrieving Texts based on Abstract Descriptions

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2023-06-15 About

@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 About

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 About

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 About

Understanding Semantic Search and Question Answering | deepset

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2022-06-03 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 through a vectorized Wikipedia (SentenceBERT) with the Weaviate vector search engine

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2021-12-05 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

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

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2021-04-12 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

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2017-06-09 About

Thesaurus-augmented Search with Jena Text | ZBW Labs

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2013-08-12 About

Using SKOS vocabularies for improving Web Search

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2013-05-30 About

lucene-skos - A SKOS analyzer module for Apache Lucene and Solr - Google Project Hosting

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2012-05-04 About

Modeling Uncertainty in Semantic Web Taxonomies

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2006-12-06 About