Browse > Methodology > Word Embeddings > Learning Word Embeddings

Learning Word Embeddings

12 papers with code · Methodology

Leaderboards

No evaluation results yet. Help compare methods by submit evaluation metrics.

Greatest papers with code

Few-Shot Representation Learning for Out-Of-Vocabulary Words

ACL 2019 acbull/HiCE

Existing approaches for learning word embeddings often assume there are sufficient occurrences for each word in the corpus, such that the representation of words can be accurately estimated from their contexts.

LEARNING WORD EMBEDDINGS META-LEARNING REGRESSION

Speech2Vec: A Sequence-to-Sequence Framework for Learning Word Embeddings from Speech

23 Mar 2018iamyuanchung/speech2vec-pretrained-vectors

In this paper, we propose a novel deep neural network architecture, Speech2Vec, for learning fixed-length vector representations of audio segments excised from a speech corpus, where the vectors contain semantic information pertaining to the underlying spoken words, and are close to other vectors in the embedding space if their corresponding underlying spoken words are semantically similar.

LEARNING WORD EMBEDDINGS

Variational Sequential Labelers for Semi-Supervised Learning

EMNLP 2018 mingdachen/vsl

Our model family consists of a latent-variable generative model and a discriminative labeler.

LEARNING WORD EMBEDDINGS

Skip-gram word embeddings in hyperbolic space

30 Aug 2018lateral/minkowski

Recent work has demonstrated that embeddings of tree-like graphs in hyperbolic space surpass their Euclidean counterparts in performance by a large margin.

LEARNING WORD EMBEDDINGS

Grammatical Error Detection Using Error- and Grammaticality-Specific Word Embeddings

IJCNLP 2017 kanekomasahiro/grammatical-error-detection

In this study, we improve grammatical error detection by learning word embeddings that consider grammaticality and error patterns.

GRAMMATICAL ERROR DETECTION LEARNING WORD EMBEDDINGS

The Mixing method: low-rank coordinate descent for semidefinite programming with diagonal constraints

1 Jun 2017locuslab/mixing

In this paper, we propose a low-rank coordinate descent approach to structured semidefinite programming with diagonal constraints.

LEARNING WORD EMBEDDINGS