Semanlink - Representation learning ; Text: similarity, embeddings... AND Word embeddings

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Representation learning ; Text: similarity, embeddings... AND Word embeddings

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28 Documents

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

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2021-09-30 About

A Self-Training Approach for Short Text Clustering - (Hadifar 2019)

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2021-05-20 About

[1910.04126] Scalable Nearest Neighbor Search for Optimal Transport

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2020-02-20 About

Text similarity search in Elasticsearch using vector fields | Elastic Blog

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2020-01-10 About

[1703.02507] Unsupervised Learning of Sentence Embeddings using Compositional n-Gram Features

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2019-03-25 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

Unsupervised Random Walk Sentence Embeddings: A Strong but Simple Baseline (Ethayarajh 2018)

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2018-10-08 About

A La Carte Embedding: Cheap but Effective Induction of Semantic Feature Vectors (2018)

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A La Carte Embedding

2018-09-18 About

Simple and efficient semantic embeddings for rare words, n-grams, and language features – Off the convex path

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2018-09-18 About

what are the pros and cons of the various unsupervised word and sentence/ document embedding models? - Quora

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2018-08-19 About

Deep-learning-free Text and Sentence Embedding, Part 2 – Off the convex path

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2018-06-25 About

Deep-learning-free Text and Sentence Embedding, Part 1 – Off the convex path

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2018-06-25 About

[1806.06259] Evaluation of sentence embeddings in downstream and linguistic probing tasks

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2018-06-19 About

The Current Best of Universal Word Embeddings and Sentence Embeddings (2018)

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2018-05-30 About

A Simple but Tough-to-Beat Baseline for Sentence Embeddings (2017)

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2018-05-10 About

A gentle introduction to Doc2Vec – ScaleAbout – Medium

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Doc2Vec

2018-02-14 About

Explanation for Doc2Vec - Quora

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Doc2Vec

2018-02-14 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

[1607.01759] Bag of Tricks for Efficient Text Classification

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2017-09-10 About

[1507.07998] Document Embedding with Paragraph Vectors

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2017-08-20 About

[1405.4053] Distributed Representations of Sentences and Documents

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2017-07-10 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