Previous models for learning the semantic vectors of items and their groups,
such as words, sentences, nodes, and graphs, using distributed representation
have been based on the assumption that an item corresponds to one vector
composed of dimensions corresponding to hidden contexts in the target. Multiple
senses of an item are represented by assigning a vector to each of the domains
where the item may appear or reflecting the context to the sense of the item.
However, there may be multiple distinct senses of an item that change or evolve
dynamically, according to the contextual shift or the emergence of novel
contexts even within one domain, similar to a living entity evolving with
environmental shifts. Setting the scope of disambiguity of items for
sensemaking, the author presents a method in which a word or item in the data
embraces multiple semantic vectors that evolve via interaction with others,
similar to a cell embracing chromosomes crossing over with each other. We
obtained two preliminary results: (1) the role of a word that evolves to
acquire the largest or lower-middle variance of semantic vectors tends to be
explainable by the author of the text; (2) the epicenters of earthquakes that
acquire larger variance via crossover, corresponding to the interaction with
diverse areas of land crust, are likely to correspond to the epicenters of
forthcoming large earthquakes.

用分布式表示构建多义词语义向量，通过与其他向量交互演化而成，研究结果显示，具有较大或中等方差的词在文本中较容易解释，而通过与不同地壳区域交互获得更大方差的震中可能对应即将发生的大地震震中。

语义单元：获取物品感知多样性的进化过程

Semantic Cells: Evolutional Process to Acquire Sense Diversity of Items

In traditional Distributional Semantic Models (DSMs) the multiple senses of a
polysemous word are conflated into a single vector space representation. In
this work, we propose a DSM that learns multiple distributional representations
of a word based on different topics. First, a separate DSM is trained for each
topic and then each of the topic-based DSMs is aligned to a common vector
space. Our unsupervised mapping approach is motivated by the hypothesis that
words preserving their relative distances in different topic semantic
sub-spaces constitute robust \textit{semantic anchors} that define the mappings
between them. Aligned cross-topic representations achieve state-of-the-art
results for the task of contextual word similarity. Furthermore, evaluation on
NLP downstream tasks shows that multiple topic-based embeddings outperform
single-prototype models.

本文提出了一种基于不同主题学习单词的多个分布式表示的分布式语义模型，该模型通过不同主题训练不同的 DSM，然后将每个基于主题的 DSM 对齐到一个公共向量空间，实现了最先进的上下文词语相似性任务，并且在 NLP 下游任务中表现出比单一原型模型更好的性能。