Span-based nested named-entity recognition (NER) has a cubic-time complexity
using a variant of the CYK algorithm. We show that by adding a supplementary
structural constraint on the search space, nested NER has a quadratic-time
complexity, that is the same asymptotic complexity than the non-nested case.
The proposed algorithm covers a large part of three standard English benchmarks
and delivers comparable experimental results.

通过在搜索空间上添加补充结构约束，命名实体识别中的嵌套是具有二次时间复杂度的，提出的算法在三个标准英语基准测试中所覆盖的范围广泛，并提供可比较的实验结果。

一种 O (n^2) 的基于跨度的嵌套命名实体识别动态规划算法

A dynamic programming algorithm for span-based nested named-entity recognition in O(n^2)

Recent studies uncovered important core/periphery network structures
characterizing complex sets of cooperative and competitive interactions between
network nodes, be they proteins, cells, species or humans. Better
characterization of the structure, dynamics and function of core/periphery
networks is a key step of our understanding cellular functions, species
adaptation, social and market changes. Here we summarize the current knowledge
of the structure and dynamics of "traditional" core/periphery networks,
rich-clubs, nested, bow-tie and onion networks. Comparing core/periphery
structures with network modules, we discriminate between global and local
cores. The core/periphery network organization lies in the middle of several
extreme properties, such as random/condensed structures, clique/star
configurations, network symmetry/asymmetry, network
assortativity/disassortativity, as well as network hierarchy/anti-hierarchy.
These properties of high complexity together with the large degeneracy of core
pathways ensuring cooperation and providing multiple options of network flow
re-channelling greatly contribute to the high robustness of complex systems.
Core processes enable a coordinated response to various stimuli, decrease
noise, and evolve slowly. The integrative function of network cores is an
important step in the development of a large variety of complex organisms and
organizations. In addition to these important features and several decades of
research interest, studies on core/periphery networks still have a number of
unexplored areas.

通过总结核心 / 外围网络、富人俱乐部、嵌套、蝴蝶结和洋葱网络的结构和动态的当前知识，研究了全局和局部核心之间的差异，揭示了这些高度复杂性的特征及如何影响复杂系统的韧性。