Vector-space representations provide geometric tools for reasoning about the
similarity of a set of objects and their relationships. Recent machine learning
methods for deriving vector-space embeddings of words (e.g., word2vec) have
achieved considerable success in natural language processing. These vector
spaces have also been shown to exhibit a surprising capacity to capture verbal
analogies, with similar results for natural images, giving new life to a
classic model of analogies as parallelograms that was first proposed by
cognitive scientists. We evaluate the parallelogram model of analogy as applied
to modern word embeddings, providing a detailed analysis of the extent to which
this approach captures human relational similarity judgments in a large
benchmark dataset. We find that that some semantic relationships are better
captured than others. We then provide evidence for deeper limitations of the
parallelogram model based on the intrinsic geometric constraints of vector
spaces, paralleling classic results for first-order similarity.

本文评估了将平行四边形模型应用于现代单词嵌入的类比关系能够捕捉基于人类关系相似度判断的能力，并发现其在某些语义关系上表现更好，但同时提供了基于向量空间内在几何约束的更深层次局限性证据，对一阶相似性产生的古典结果进行类比。

评估向量空间类比模型

Evaluating vector-space models of analogy

There have been many recent advances in the structure and measurement of
distributed language models: those that map from words to a vector-space that
is rich in information about word choice and composition. This vector-space is
the distributed language representation. The goal of this note is to point out
that any distributed representation can be turned into a classifier through
inversion via Bayes rule. The approach is simple and modular, in that it will
work with any language representation whose training can be formulated as
optimizing a probability model. In our application to 2 million sentences from
Yelp reviews, we also find that it performs as well as or better than complex
purpose-built algorithms.

本研究论文讨论了结构和测量分布式语言模型的最新进展，指出任何分布式表示都可以通过贝叶斯原理转化为分类器，并且在 Yelp 评论的应用实验中表现良好。

通过分布式语言表示的反演进行文档分类

Document Classification by Inversion of Distributed Language  Representations

There is rising interest in vector-space word embeddings and their use in
NLP, especially given recent methods for their fast estimation at very large
scale. Nearly all this work, however, assumes a single vector per word type
ignoring polysemy and thus jeopardizing their usefulness for downstream tasks.
We present an extension to the Skip-gram model that efficiently learns multiple
embeddings per word type. It differs from recent related work by jointly
performing word sense discrimination and embedding learning, by
non-parametrically estimating the number of senses per word type, and by its
efficiency and scalability. We present new state-of-the-art results in the word
similarity in context task and demonstrate its scalability by training with one
machine on a corpus of nearly 1 billion tokens in less than 6 hours.

提出一种扩展 Skip-gram 模型的方法，它可以高效地学习每个单词类型的多个嵌入，通过联合进行词义辨别和嵌入学习，非参数地估计每个单词类型的很多不同的词义，并通过在一个拥有近 10 亿标记的语料库上训练一台机器的演示，展示了它的可扩展性。