语义细胞：获取项目意义多样性的演化过程
Semantic Cells: Evolutional Process to Acquire Sense Diversity of Items
AI总结：
本文提出语义细胞方法，使项目包含多个通过交互演化的语义向量以应对动态意义变化，并在文本作者解释和地震预测中取得初步验证。
摘要：
以往使用分布式表示学习项目（如单词、句子、节点和图）及其群组的语义向量的模型，都基于一个假设：一个项目的基本意义对应一个由与目标现实世界中隐藏上下文相对应的维度组成的向量，通过遵从词汇数据库或适应上下文，从中获得该项目的多种意义。然而，一个项目可能存在多种意义，这些意义难以被同化，并且即使在同一文档或受限时期内，也会随着上下文的转变而动态变化或演化。这是一个类似于生物实体随环境变化而演化或适应的过程。设定消歧的范围以进行意义构建，作者提出了一种方法，其中数据中的单词或项目包含多个通过与其他项目交互而演化的语义向量，类似于包含相互交叉的染色体的细胞。我们获得了两个初步结果：（1）演化后获得语义向量最大或中下方差的单词语义角色，往往能被文本作者所解释；（2）通过交叉获得更大方差的地震震中（对应于与地壳不同区域的相互作用），很可能对应于即将发生的大地震的震中。
英文摘要：
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 the basic sense of an item corresponds to one vector composed of dimensions corresponding to hidden contexts in the target real world, from which multiple senses of the item are obtained by conforming to lexical databases or adapting to the context. However, there may be multiple senses of an item, which are hardly assimilated and change or evolve dynamically following the contextual shift even within a document or a restricted period. This is a process similar to the evolution or adaptation of a living entity with/to environmental shifts. Setting the scope of disambiguation 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
