In this paper, we approach Vietnamese word segmentation as a binary
classification by using the Support Vector Machine classifier. We inherit
features from prior works such as n-gram of syllables, n-gram of syllable
types, and checking conjunction of adjacent syllables in the dictionary. We
propose two novel ways to feature extraction, one to reduce the overlap
ambiguity and the other to increase the ability to predict unknown words
containing suffixes. Different from UETsegmenter and RDRsegmenter, two
state-of-the-art Vietnamese word segmentation methods, we do not employ the
longest matching algorithm as an initial processing step or any post-processing
technique. According to experimental results on benchmark Vietnamese datasets,
our proposed method obtained a better F1-score than the prior state-of-the-art
methods UETsegmenter, and RDRsegmenter.

使用支持向量机分类器的越南语单词分割方法通过采用音节的 n-gram、音节类型的 n-gram 和在词典中检查相邻音节的连接等方面，继承了以前的工作特征，提出了两种新的特征提取方法，一种是减少重叠歧义，另一种是增加预测包含后缀的未知单词的能力，在基准越南语数据集上，我们提出的方法获得了比先前最先进的方法 UETsegmenter 和 RDRsegmenter 更好的 F1 分数。

基于 SVM 的越南语分词：减少歧义和捕获后缀

Vietnamese Word Segmentation with SVM: Ambiguity Reduction and Suffix  Capture

When evaluating the efficacy of social programs and medical treatments using
randomized experiments, the estimated overall average causal effect alone is
often of limited value and the researchers must investigate when the treatments
do and do not work. Indeed, the estimation of treatment effect heterogeneity
plays an essential role in (1) selecting the most effective treatment from a
large number of available treatments, (2) ascertaining subpopulations for which
a treatment is effective or harmful, (3) designing individualized optimal
treatment regimes, (4) testing for the existence or lack of heterogeneous
treatment effects, and (5) generalizing causal effect estimates obtained from
an experimental sample to a target population. In this paper, we formulate the
estimation of heterogeneous treatment effects as a variable selection problem.
We propose a method that adapts the Support Vector Machine classifier by
placing separate sparsity constraints over the pre-treatment parameters and
causal heterogeneity parameters of interest. The proposed method is motivated
by and applied to two well-known randomized evaluation studies in the social
sciences. Our method selects the most effective voter mobilization strategies
from a large number of alternative strategies, and it also identifies the
characteristics of workers who greatly benefit from (or are negatively affected
by) a job training program. In our simulation studies, we find that the
proposed method often outperforms some commonly used alternatives.

本文研究对社会项目和医疗方案的疗效进行随机实验评估时，如何准确评估其异质性治疗效应，进而为大量可用的治疗方案选择最有效的方案，确定治疗成功或失败的人群，以及设计个性化的最佳治疗方案。本文提出一种利用支持向量机分类器适应变量选择问题进行异质性治疗效应评估的方法，并证明其优于常用替代方法。