State-of-the-art models of lexical semantic change detection suffer from
noise stemming from vector space alignment. We have empirically tested the
Temporal Referencing method for lexical semantic change and show that, by
avoiding alignment, it is less affected by this noise. We show that, trained on
a diachronic corpus, the skip-gram with negative sampling architecture with
temporal referencing outperforms alignment models on a synthetic task as well
as a manual testset. We introduce a principled way to simulate lexical semantic
change and systematically control for possible biases.

通过时间参照等方法来完成词汇语义变化检测，比传统的向量空间对齐方法能够更准确地控制误差。在使用时序参照的 Skip-gram 负采样架构和以往方法进行对比后，实验证明该方法在一个合成任务和一个人工测试集上的性能更佳。同时，我们介绍了一种模拟词汇语义变化并系统性地控制可能偏见的方法。

Time-Out: 模型词汇语义变化的时间参考方法

Time-Out: Temporal Referencing for Robust Modeling of Lexical Semantic  Change

Most network embedding algorithms consist in measuring co-occurrences of
nodes via random walks then learning the embeddings using Skip-Gram with
Negative Sampling. While it has proven to be a relevant choice, there are
alternatives, such as GloVe, which has not been investigated yet for network
embedding. Even though SGNS better handles non co-occurrence than GloVe, it has
a worse time-complexity. In this paper, we propose a matrix factorization
approach for network embedding, inspired by GloVe, that better handles non
co-occurrence with a competitive time-complexity. We also show how to extend
this model to deal with networks where nodes are documents, by simultaneously
learning word, node and document representations. Quantitative evaluations show
that our model achieves state-of-the-art performance, while not being so
sensitive to the choice of hyper-parameters. Qualitatively speaking, we show
how our model helps exploring a network of documents by generating
complementary network-oriented and content-oriented keywords.

本文提出了一种矩阵分解方法，受到 GloVe 启发，用于网络节点嵌入，同时学习单词、节点和文档表示，该模型表现出良好的性能和鲁棒性，可用于探索文件网络，并生成互补的网络和内容关键字。

全局节点表示向量

Global Vectors for Node Representations

A surprising property of word vectors is that word analogies can often be
solved with vector arithmetic. However, it is unclear why arithmetic operators
correspond to non-linear embedding models such as skip-gram with negative
sampling (SGNS). We provide a formal explanation of this phenomenon without
making the strong assumptions that past theories have made about the vector
space and word distribution. Our theory has several implications. Past work has
conjectured that linear substructures exist in vector spaces because relations
can be represented as ratios; we prove that this holds for SGNS. We provide
novel justification for the addition of SGNS word vectors by showing that it
automatically down-weights the more frequent word, as weighting schemes do ad
hoc. Lastly, we offer an information theoretic interpretation of Euclidean
distance in vector spaces, justifying its use in capturing word dissimilarity.

研究发现，使用向量算术操作可以解决单词类比问题，本文提供了 skip-gram 模型中算术运算符对应的非线性嵌入模型的正式解释，证明了线性子结构存在于向量空间中，通过从技术上的角度证明了降低高频词权重的有效性以及欧氏距离在捕捉单词不相似性问题上的信息论解释。