Semanlink - Information retrieval AND Embeddings

1. synthetic queries
are generated for each passage from the target corpus (using an existing pre-trained [T5](tag:text_to_text_transfer_transformer)
encoder-decoder)
2. the generated queries are used for mining negative
passages (retrieving the most similar
paragraphs using an existing dense retrieval
model == hard negatives!)
3. the query-passage pairs are labeled by a cross-encoder and used to train the domain-adapted
dense retriever (using method described in [Hofstätter et al.,
2020](doc:2021/12/2010_02666_improving_efficien))

[Nils Reimers sur Twitter](doc:2021/12/nils_reimers_sur_twitter_do_), [GitHub](https://github.com/UKPLab/gpl),  by the author of [TSDAE](doc:2021/09/2104_06979_tsdae_using_trans)

Claims to improve "Doc2Query" [Document Expansion by Query Prediction](doc:2022/01/1904_08375_document_expansion): ([src](https://twitter.com/KexinWang2049/status/1471435779415150598))

> - GPL: Uses doc2query to construct synthetic data and does knowledge distillation (i.e. training) on that data.
> - Doc2query: Generates queries to extend the documents and use BM25 on top of them w/o training.

2021-12-15 A propos

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

Tags:

2021-12-05 A propos

Unsupervised Training for Sentence Transformers | Pinecone

Tags:

2021-11-24 A propos

Andrea Volpini sur Twitter : New blog post on #ContentHub for #Semantic #SEO. How to cluster content using Knowledge Graph Embeddings to detect: ✓ Clusters ✓ Sub-topics ✓ Author's topical authority

Tags:

2021-11-16 A propos

Multilingual Sentence Transformers | Pinecone

Tags:

2021-11-04 A propos

Next-Gen Sentence Embeddings with Multiple Negatives Ranking Loss | Pinecone

Tags:

2021-10-27 A propos

Sentence Embeddings and Transformers | Pinecone

Tags:

2021-10-23 A propos

[2109.08133] Phrase Retrieval Learns Passage Retrieval, Too

Tags:

2021-09-30 A propos

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

Tags:

2021-09-30 A propos

Nils Reimers sur Twitter : "New models for Neural Information Retrieval..."

Tags:

2021-04-17 A propos

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

Tags:

2021-04-12 A propos

[2004.06842] Layered Graph Embedding for Entity Recommendation using Wikipedia in the Yahoo! Knowledge Graph

Tags:

2020-04-17 A propos

[1911.02168] CoKE: Contextualized Knowledge Graph Embedding

Tags:

2020-03-22 A propos

[1910.04126] Scalable Nearest Neighbor Search for Optimal Transport

Tags:

2020-02-20 A propos

Tags:

2020-01-10 A propos

[1910.03524] Beyond Vector Spaces: Compact Data Representation as Differentiable Weighted Graphs

Tags:

2019-10-09 A propos

[1908.10084] Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

Tags:

2019-08-28 A propos

[1602.01137] A Dual Embedding Space Model for Document Ranking

Tags:

2019-07-17 A propos

Sent2Vec: An unsupervised approach towards learning sentence embeddings | RARE Technologies

Tags:

2019-03-25 A propos

Cross-Modal Retrieval in the Cooking Context

Tags:

2018-07-11 A propos

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

Tags:

2018-07-09 A propos

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

Tags:

2018-06-08 A propos

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

Tags:

2018-05-26 A propos

Tutorial on Poincaré Embeddings (Jupyter Notebook )

Tags:

2018-05-20 A propos

Implementing Poincaré Embeddings | RARE Technologies

Tags:

2018-05-20 A propos

[1710.04099] Wembedder: Wikidata entity embedding web service

Tags:

2018-02-13 A propos

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

Tags:

2018-02-12 A propos

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

Tags:

> 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 A propos

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

Tags:

2017-12-30 A propos

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

Tags: