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Favoris ; Neural networks AND Text Similarity

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Word Mover’s Distance

23 Documents

LoRA for semantic similarity tasks

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2023-07-21 About

Jerry Liu sur Twitter : "Using cross-encoding as a reranking step can dramatically speed up LLM inference time AND improve accuracy!"

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2023-07-20 About

Making the Most of Data: Augmentation with BERT | Pinecone

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2021-12-18 About

Using pretrained SBERT model in cross-encoder · Issue #726 · UKPLab/sentence-transformers

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2021-12-17 About

Advance BERT model via transferring knowledge from Cross-Encoders to Bi-Encoders | by Chien Vu | Towards Data Science

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2021-12-17 About

Finding similar documents with transformers · Codegram

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2020-07-10 About

[1910.04126] Scalable Nearest Neighbor Search for Optimal Transport

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2020-02-20 About

Semantic Text Matching for Long-Form Documents (2019)

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2020-01-23 About

Siamese Network for Image and Text similarity using Keras

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2020-01-22 About

Text similarity search in Elasticsearch using vector fields | Elastic Blog

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2020-01-10 About

Elasticsearch meets BERT: Building Search Engine with Elasticsearch and BERT

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2020-01-10 About

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

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2019-08-28 About

Learning Text Similarity with Siamese Recurrent Networks (2016)

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2019-08-07 About

Classifying duplicate questions from Quora with Keras | R-bloggers

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2019-07-03 About

nlp-notebooks/Simple Sentence Similarity.ipynb at master · nlptown/nlp-notebooks

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2019-02-07 About

Word Mover's Embedding: From Word2Vec to Document Embedding (2018)

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2018-11-10 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

gensim/WMD_tutorial.ipynb

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2017-12-23 About

Calculates Word Mover's Distance Insanely Fast

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2017-11-12 About

Word Mover’s Distance in Python

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Word Mover’s Distance

2017-11-12 About

A Comparison of Vector-based Representations for Semantic Composition (Blacoe and Lapata - 2012)

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2017-11-12 About

From Word Embeddings To Document Distances (Kusner 2015)

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Word Mover’s Distance

2017-06-09 About

Short Text Similarity with Word Embeddings

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2017-05-18 About