Network embedding methods aim at learning low-dimensional latent representation of nodes in a network. These representations can be used as features for a wide range of tasks on graphs such as classification, clustering, link prediction, and visuali
Reasoning is essential for the development of large knowledge graphs,especiallyforcompletion,whichaimstoinfernewtriples basedonexistingones.Bothrulesandembeddingscanbeusedfor knowledgegraphreasoningandtheyhavetheirownadvantages anddifficulties.Rule-
While one of the first steps in many NLP systems is selecting what pre-trained word embeddings to use, we argue that such a step is
better left for neural networks to figure out
by themselves. To that end, we introduce
dynamic meta-embeddings, a s
contextual embedding 综述 Contextual embeddings, such as ELMo and
BERT, move beyond global word represen-
tations like Word2Vec and achieve ground-
breaking performance on a wide range of natu-
ral language processing tasks. Contextual em-
beddings ass
超级嵌入：超级解释，预测和利用的统一模型
（工作正在进行中）
ACL 2016文章的源代码，数据和补充材料。 请使用以下引用：
inproceedings{Flekova.Gurevych.2016.ACL,
author = {Lucie Flekova and Iryna Gurevych},
title = {Supersense Embeddings: A Unified Model for Supersense Interpretation,
Prediction, and U
