Semanlink - NLP tasks / problems ; Word embeddings AND Using word embeddings

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NLP tasks / problems ; Word embeddings AND Using word embeddings

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Word Mover’s Distance

21 Documents

Towards Unsupervised Text Classification Leveraging Experts and Word Embeddings - (ACL 2019)

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2020-10-05 About

Unsupervised text classification with word embeddings - Max Halford

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2020-10-05 About

[1910.04126] Scalable Nearest Neighbor Search for Optimal Transport

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2020-02-20 About

davidsbatista/BREDS: "Bootstrapping Relationship Extractors with Distributional Semantics" (Batista et al., 2015) - code for EMNLP'15 paper

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2020-01-11 About

Text similarity search in Elasticsearch using vector fields | Elastic Blog

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2020-01-10 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

[1710.06632] Towards a Seamless Integration of Word Senses into Downstream NLP Applications

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2018-10-09 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

machine learning - Text categorization: combining different kind of features - Data Science Stack Exchange

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2017-12-06 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

Monolingual and Cross-Lingual Information Retrieval Models Based on (Bilingual) Word Embeddings (2015)

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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

Using pre-trained word embeddings in a Keras model

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2017-10-23 About

Named Entity Recognition using Word Embedding as a Feature (2016)

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2017-10-01 About

Semantic expansion using word embedding clustering and convolutional neural network for improving short text classification - ScienceDirect

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2017-07-21 About

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

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2017-06-09 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