Learning Word Embeddings

18 papers with code • 0 benchmarks • 0 datasets

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Greatest papers with code

Neural Graph Embedding Methods for Natural Language Processing

svjan5/gnns-for-nlp 8 Nov 2019

Knowledge graphs are structured representations of facts in a graph, where nodes represent entities and edges represent relationships between them.

Graph Embedding Knowledge Graphs +3

Dict2vec : Learning Word Embeddings using Lexical Dictionaries

tca19/dict2vec EMNLP 2017

Learning word embeddings on large unlabeled corpus has been shown to be successful in improving many natural language tasks.

General Classification Knowledge Graphs +7

Towards Incremental Learning of Word Embeddings Using Context Informativeness

minimalparts/nonce2vec ACL 2019

In this paper, we investigate the task of learning word embeddings from very sparse data in an incremental, cognitively-plausible way.

Incremental Learning Learning Word Embeddings

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

acbull/HiCE ACL 2019

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.

Few-shot Regression Learning Word Embeddings

WordRank: Learning Word Embeddings via Robust Ranking

shihaoji/wordrank EMNLP 2016

Then, based on this insight, we propose a novel framework WordRank that efficiently estimates word representations via robust ranking, in which the attention mechanism and robustness to noise are readily achieved via the DCG-like ranking losses.

Learning Word Embeddings Word Similarity

Skip-gram word embeddings in hyperbolic space

lateral/minkowski 30 Aug 2018

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 Word Similarity