Semanlink - Good ; Neural networks AND Information retrieval

SPLADE for Sparse Vector Search Explained | Pinecone

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2023-09-22 About

[2305.12517] Retrieving Texts based on Abstract Descriptions

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2023-06-15 About

Building Scalable, Explainable, and Adaptive NLP Models with Retrieval | SAIL Blog

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2021-10-07 About

SIGIR 2018 Tutorial - Knowledge Extraction and Inference from Text: Shallow, Deep, and Everything in Between

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2018-07-09 About

A Theoretical Approach to Semantic Coding and Hashing | Simons Institute for the Theory of Computing (2016)

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2018-05-26 About

Semantic hashing (2008) - Ruslan Salakhutdinov, Geoffrey Hinton

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> We show how to learn a deep graphical model of the word-count vectors obtained from a
large set of documents. The values of the latent variables in the deepest layer are easy to
infer and give a much better representation of each document than Latent Semantic Analysis.
When the deepest layer is forced to use a small number of binary variables (e.g. 32),
the graphical model performs ‘‘semantic hashing”: Documents are mapped to memory
addresses in such a way that semantically similar documents are located at nearby
addresses. Documents similar to a query document can then be found by simply accessing
all the addresses that differ by only a few bits from the address of the query document. This
way of extending the efficiency of hash-coding to approximate matching is much faster
than locality sensitive hashing, which is the fastest current method. By using semantic
hashing to filter the documents given to TF-IDF, we achieve higher accuracy than applying
TF-IDF to the entire document set.

Indexing is implemented in the following manner: a document is mapped to a word-count vector and then this vector is passed through a [#Restricted Boltzmann Machine](/tag/restricted_boltzmann_machine) autoencoder and encoded to 32-bit address.

2017-11-07 About