Semanlink - Embeddings in Information Retrieval

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Embeddings in Information Retrieval

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Vector Retrieval

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DSSM (Deep Semantic Similarity Model)

14 Documents (Short List)

[2103.05256] CEQE: Contextualized Embeddings for Query Expansion

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2023-10-28 About

SparseEmbed: Learning Sparse Lexical Representations with Contextual Embeddings for Retrieval

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2023-07-26 About

[2305.12517] Retrieving Texts based on Abstract Descriptions

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2023-06-15 About

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

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2021-04-12 About

[1602.01137] A Dual Embedding Space Model for Document Ranking

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2019-07-17 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 Tri-Partite Neural Document Language Model for Semantic Information Retrieval (2018 - ESWC conference)

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2018-06-08 About

Modèle Neuronal de Recherche d'Information Augmenté par une Ressource Sémantique (2017)

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2018-02-12 About

Evaluating the Impact of Word Embeddings on Similarity Scoring in Practical Information Retrieval (2017)

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> Transferring the success of word embeddings to Information Retrieval (IR) task is currently an active research topic. While embedding-based retrieval models could tackle the vocabulary mismatch problem by making use of the embedding’s inherent similarity between distinct words, most of them struggle to compete with the prevalent strong baselines such as TF-IDF and BM25.

Considering a practical ad-hoc IR task composed of two steps, matching and scoring, compares the performance of several techniques that leverage word embeddings in the retrieval models to compute the similarity between the query and the documents (namely word centroid similarity, paragraph vectors, Word Mover’s distance, as well as a novel inverse document frequency (IDF) re-weighted word centroid similarity).

> We confirm that word embeddings can be successfully employed in a practical information retrieval setting. The proposed cosine similarity of IDF re-weighted, aggregated word vectors is competitive to the TF-IDF baseline.

2018-01-28 About

Learning Deep Structured Semantic Models for Web Search using Clickthrough Data - Microsoft Research (2013)

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2017-12-30 About

DSSM ("Deep Semantic Similarity Model") - Microsoft Research

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DSSM (Deep Semantic Similarity Model)

2017-12-30 About

Distributed Word Representations for Information Retrieval

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2017-10-01 About

Vectorland: Brief Notes from Using Text Embeddings for Search

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2017-09-18 About

Using Text Embeddings for Information Retrieval

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2017-09-18 About

Properties

sl:creationDate : 2018-01-28
sl:creationTime : 2018-01-28T17:20:14Z
rdf:type : sl:Tag
skos:altLabel : Embeddings in IR@en
skos:prefLabel : Embeddings in Information Retrieval@en