Semanlink - Information retrieval ; Embeddings AND NLP@Google

Printer friendly

Search Tag:

Search Doc:

Preferences...

Tag Cloud

Information retrieval ; Embeddings AND NLP@Google

Common descendants

23 Documents

SparseEmbed: Learning Sparse Lexical Representations with Contextual Embeddings for Retrieval

Tags:

2023-07-26 About

Domain Adaptation with Generative Pseudo-Labeling (GPL) | Pinecone

Tags:

2023-04-09 About

Classifying long textual documents (up to 25 000 tokens) using BERT | by Sinequa | (2020)

Tags:

2023-04-07 About

Multilingual Sentence Transformers | Pinecone

Tags:

2023-01-13 About

[2205.11498] Domain Adaptation for Memory-Efficient Dense Retrieval

Tags:

2022-09-26 About

NAVER LABS Europe : "@Nils_Reimers of @huggingface on 'Unsupervised domain adaptation for neural search'"

Tags:

2022-03-09 About

[2112.07577] GPL: Generative Pseudo Labeling for Unsupervised Domain Adaptation of Dense Retrieval

Tags:

An unsupervised domain adaptation technique for dense retrieval models

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 About

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

Tags:

2021-12-05 About

Unsupervised Training for Sentence Transformers | Pinecone

Tags:

2021-11-24 About

Multilingual Sentence Transformers | Pinecone

Tags:

2021-11-04 About

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

Tags:

2021-10-27 About

Sentence Embeddings and Transformers | Pinecone

Tags:

2021-10-23 About

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

Tags:

2021-04-17 About

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

Tags:

2021-04-12 About

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

Tags:

2019-08-28 About

[1710.04099] Wembedder: Wikidata entity embedding web service

Tags:

2018-02-13 About

gensim/WMD_tutorial.ipynb

Tags:

2017-12-23 About

Using Gensim Word2Vec Embeddings in Keras | Ben Bolte's Blog

Tags:

2017-10-23 About

Distributed Word Representations for Information Retrieval

Tags:

2017-10-01 About

An Intuitive Understanding of Word Embeddings: From Count Vectors to Word2Vec

Tags:

2017-06-09 About

Tags:

2017-06-09 About

gensim: models.word2vec – Deep learning with word2vec

Tags:

2017-06-01 About

Word2vec in gensim Tutorial | RaRe Technologies

Tags:

2017-06-01 About