In Distributional Semantic Models (DSMs), Vector Cosine is widely used to
estimate similarity between word vectors, although this measure was noticed to
suffer from several shortcomings. The recent literature has proposed other
methods which attempt to mitigate such biases. In this paper, we intend to
investigate APSyn, a measure that computes the extent of the intersection
between the most associated contexts of two target words, weighting it by
context relevance. We evaluated this metric in a similarity estimation task on
several popular test sets, and our results show that APSyn is in fact highly
competitive, even with respect to the results reported in the literature for
word embeddings. On top of it, APSyn addresses some of the weaknesses of Vector
Cosine, performing well also on genuine similarity estimation.

本研究旨在探究一种新的度量方法 APSyn，在相似度估计任务中表现出了高的竞争力，甚至与单词嵌入的结果报告相当，并解决了 Vector Cosine 的一些弱点。

用 APSyn 测试向量余弦相似度估计

Testing APSyn against Vector Cosine on Similarity Estimation

In this paper, we claim that vector cosine, which is generally considered
among the most efficient unsupervised measures for identifying word similarity
in Vector Space Models, can be outperformed by an unsupervised measure that
calculates the extent of the intersection among the most mutually dependent
contexts of the target words. To prove it, we describe and evaluate APSyn, a
variant of the Average Precision that, without any optimization, outperforms
the vector cosine and the co-occurrence on the standard ESL test set, with an
improvement ranging between +9.00% and +17.98%, depending on the number of
chosen top contexts.

本文认为矢量余弦在向量空间模型中用于识别单词相似度的无监督度量之一，可以被计算目标词最互相关联上下文的交集的无监督度量领先。为了证明这一点，我们描述并评估了 APSyn，它是平均精度的一种变体，无需任何优化，在标准 ESL 测试集上比矢量余弦和共现提高了 9.00％至 17.98％，具体取决于选择的顶部上下文的数量。

无监督的词语相似度测量方法：如何在向量空间模型中超越共现和向量余弦

Unsupervised Measure of Word Similarity: How to Outperform Co-occurrence  and Vector Cosine in VSMs

In this paper, we claim that Vector Cosine, which is generally considered one
of the most efficient unsupervised measures for identifying word similarity in
Vector Space Models, can be outperformed by a completely unsupervised measure
that evaluates the extent of the intersection among the most associated
contexts of two target words, weighting such intersection according to the rank
of the shared contexts in the dependency ranked lists. This claim comes from
the hypothesis that similar words do not simply occur in similar contexts, but
they share a larger portion of their most relevant contexts compared to other
related words. To prove it, we describe and evaluate APSyn, a variant of
Average Precision that, independently of the adopted parameters, outperforms
the Vector Cosine and the co-occurrence on the ESL and TOEFL test sets. In the
best setting, APSyn reaches 0.73 accuracy on the ESL dataset and 0.70 accuracy
in the TOEFL dataset, beating therefore the non-English US college applicants
(whose average, as reported in the literature, is 64.50%) and several
state-of-the-art approaches.

本文声称，对于在向量空间模型中识别单词相似度最有效的无监督度量之一，即向量余弦来说，它可以被一种完全无监督的度量超越，这种度量评估两个目标单词的最相关语境的交集程度，并根据共享语境在依赖性排名列表中的排名对该交集进行加权。为了证明这一点，我们描述并评估了 APSyn，它是平均准确率的一种变体，独立于采用的参数，其在 ESL 和 TOEFL 测试集上均优于向量余弦和共现。在最佳设置下，APSyn 在 ESL 数据集上达到 0.73 的准确率，在 TOEFL 数据集上达到 0.70 的准确率，因此超越了非英语 US 大学申请人（根据文献报道，其平均为 64.50%）和几种最先进的方法。