A review of Word Embedding Models through a deconstructive approach reveals
their several shortcomings and inconsistencies. These include instability of
the vector representations, a distorted analogical reasoning, geometric
incompatibility with linguistic features, and the inconsistencies in the corpus
data. A new theoretical embedding model, Derridian Embedding, is proposed in
this paper. Contemporary embedding models are evaluated qualitatively in terms
of how adequate they are in relation to the capabilities of a Derridian
Embedding.

该论文通过解构方法回顾了词嵌入模型，揭示了诸多缺点和不一致性，包括向量表示的不稳定性、扭曲的类比推理、与语言特征的几何不兼容性以及语料库数据的不一致性，提出了一种新的理论嵌入模型 —— 德里达式嵌入 (Derridian Embedding)，并对现代嵌入模型进行了定性评估，比较了它们与德里达式嵌入的能力是否相当。

解构词嵌入

Deconstructing Word Embeddings

We address the problem of automatically acquiring case-frame patterns from
large corpus data. In particular, we view this problem as the problem of
estimating a (conditional) distribution over a partition of words, and propose
a new generalization method based on the MDL (Minimum Description Length)
principle. In order to assist with the efficiency, our method makes use of an
existing thesaurus and restricts its attention on those partitions that are
present as `cuts' in the thesaurus tree, thus reducing the generalization
problem to that of estimating the `tree cut models' of the thesaurus. We then
give an efficient algorithm which provably obtains the optimal tree cut model
for the given frequency data, in the sense of MDL. We have used the case-frame
patterns obtained using our method to resolve pp-attachment ambiguity.Our
experimental results indicate that our method improves upon or is at least as
effective as existing methods.

本研究提出了一种新的方法来自动获取文本语料库中的案例框架模式，并使用 MDL 原则来调整模式，以帮助尽可能高效地预测文本中的结构。通过与其他方法进行对比和测试，我们证明了该方法是有效的。