论文标题
部分可观测时空混沌系统的无模型预测
A Quadratic 0-1 Programming Approach for Word Sense Disambiguation
论文作者
论文摘要
储层计算是预测湍流的有力工具，其简单的架构具有处理大型系统的计算效率。然而，其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间，然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用，该系统模拟了湍流的若干特征。我们表明，使用径向基函数作为非线性投影器，即使只有部分观测并且不知道控制方程，也能稳健地捕捉复杂的系统非线性。最后，我们表明，当测量稀疏、不完整且带有噪声，甚至控制方程变得不准确时，我们的网络仍然可以产生相当准确的预测，从而为实际湍流系统的无模型预测铺平了道路。
Word Sense Disambiguation (WSD) is the task to determine the sense of an ambiguous word in a given context. Previous approaches for WSD have focused on supervised and knowledge-based methods, but inter-sense interactions patterns or regularities for disambiguation remain to be found. We argue the following cause as one of the major difficulties behind finding the right patterns: for a particular context, the intended senses of a sequence of ambiguous words are dependent on each other, i.e. the choice of one word's sense is associated with the choice of another word's sense, making WSD a combinatorial optimization problem.In this work, we approach the interactions between senses of different target words by a Quadratic 0-1 Integer Programming model (QIP) that maximizes the objective function consisting of (1) the similarity between candidate senses of a target word and the word in a context (the sense-word similarity), and (2) the semantic interactions (relatedness) between senses of all words in the context (the sense-sense relatedness).
