剪枝Wasserstein指数生成模型及wigpy软件包
Pruned Wasserstein Index Generation Model and wigpy Package
摘要：
最近提出的Wasserstein指数生成模型（WIG）为自动生成指数指明了一个新的方向。然而，由于两个原因，在实践中拟合大型数据集是具有挑战性的。首先，Sinkhorn距离的计算是出了名的昂贵，并且严重地受到维数的影响。第二，需要计算一个完整的$N\times N$矩阵以适应记忆，其中$N$是词汇的维度。当维数太大时，甚至根本无法计算。在此，我提出了一个基于Lasso的收缩方法来降低词汇的维数，作为拟合WIG模型之前的预处理步骤。在Word 2 Vec模型中得到词嵌入后，对这些高维向量进行$k$ -均值聚类，并在每个聚类中挑选出最频繁的词作为“基础词汇”。然后，非基本标记在基本标记的向量上回归以获得转换权重，从而我们可以仅用“基本标记”来表示整个词汇表。这种变体，称为修剪WIG（pWIG），将使我们能够随意缩小词汇维度，但仍然可以达到高精度。我还在Python中提供了一个\textit{wigpy}模块来执行这两种风格的计算。经济政策不确定性（EPU）指数的应用展示与现有的方法生成时间序列的情绪指数的比较。
英文摘要：
Recent proposal of Wasserstein Index Generation model (WIG) has shown a new direction for automatically generating indices. However, it is challenging in practice to fit large datasets for two reasons. First, the Sinkhorn distance is notoriously expensive to compute and suffers from dimensionality severely. Second, it requires to compute a full $N\times N$ matrix to be fit into memory, where $N$ is the dimension of vocabulary. When the dimensionality is too large, it is even impossible to compute at all. I hereby propose a Lasso-based shrinkage method to reduce dimensionality for the vocabulary as a pre-processing step prior to fitting the WIG model. After we get the word embedding from Word2Vec model, we could cluster these high-dimensional vectors by $k$-means clustering, and pick most frequent tokens within each cluster to form the "base vocabulary". Non-base tokens are then regressed on the vectors of base token to get a transformation weight and we could thus represent the whole vocabulary by only the "base tokens". This variant, called pruned WIG (pWIG), will enable us to shrink vocabulary dimension at will but could still achieve high accuracy. I also provide a \textit{wigpy} module in Python to carry out computation in both flavor. Application to Economic Policy Uncertainty (EPU) index is showcased as comparison with existing methods of generating time-series sentiment indices.
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。
