Integrating topic modeling and word embedding to characterize violent deaths.
Integrating topic modeling and word embedding to characterize violent deaths.
复制标题
DOI：
10.1073/pnas.2108801119
复制
发表时间：
2022-03-08
影响因子：
11.1
通讯作者：
Foster JG
中科院分区：
文献类型：
--
作者：
Arseniev-Koehler A;Cochran SD;Mays VM;Chang KW;Foster JG
我们介绍了一种方法来识别大规模文本数据中的潜在主题。我们的方法集成了两个突出的计算文本分析方法：主题建模和词嵌入。我们将我们的方法应用于暴力死亡的书面叙述（例如，国家暴力死亡报告系统（National Violent Death Reporting System，NVDRS）我们的许多主题揭示了现有分类方案中没有涵盖的暴力死亡方面。我们还提取了主题本身的性别偏见（例如，关于长枪的话题特别男性化）。我们的研究结果提出了新的研究方向，可能有助于减少自杀或杀人。我们的方法广泛适用于文本数据，并可以解锁其他管理数据库中的类似信息。有一个不断升级的需要的方法，以确定潜在的模式，在文本数据从许多领域。我们介绍了一种方法来识别语料库中的主题，并表示为主题序列的文件。话语原子主题建模（DATM）借鉴了理论机器学习的进步，将主题建模和词嵌入结合起来，利用它们独特的功能。我们首先确定一组向量（“话语原子”），提供了一个稀疏表示的嵌入空间。话语原子可以被解释为潜在主题;通过生成模型，原子映射到单词上的分布。我们还可以推断出产生一系列单词的主题。我们用一个未充分利用的文本的突出例子来说明我们的方法：美国国家暴力死亡报告系统（NVDRS）。NVDRS用结构化变量和非结构化叙述总结了暴力死亡事件。我们在叙述中确定了225个潜在主题（例如，死亡和身体攻击的准备）;这些主题中有许多没有被现有的结构化变量所捕获。受性别自杀和杀人的已知模式以及最近对语义空间中性别偏见的研究的启发，我们确定了我们主题的性别偏见（例如，关于止痛药的话题是女性的）。然后，我们比较性别偏见的主题，他们的患病率在叙述女性与男性受害者。结果提供了关于致命暴力及其性别性质的报告的详细量化情况。我们的方法提供了一个灵活的和广泛适用的方法来建模文本数据中的主题。
We introduce an approach to identify latent topics in large-scale text data. Our approach integrates two prominent methods of computational text analysis: topic modeling and word embedding. We apply our approach to written narratives of violent death (e.g., suicides and homicides) in the National Violent Death Reporting System (NVDRS). Many of our topics reveal aspects of violent death not captured in existing classification schemes. We also extract gender bias in the topics themselves (e.g., a topic about long guns is particularly masculine). Our findings suggest new lines of research that could contribute to reducing suicides or homicides. Our methods are broadly applicable to text data and can unlock similar information in other administrative databases. There is an escalating need for methods to identify latent patterns in text data from many domains. We introduce a method to identify topics in a corpus and represent documents as topic sequences. Discourse atom topic modeling (DATM) draws on advances in theoretical machine learning to integrate topic modeling and word embedding, capitalizing on their distinct capabilities. We first identify a set of vectors (“discourse atoms”) that provide a sparse representation of an embedding space. Discourse atoms can be interpreted as latent topics; through a generative model, atoms map onto distributions over words. We can also infer the topic that generated a sequence of words. We illustrate our method with a prominent example of underutilized text: the US National Violent Death Reporting System (NVDRS). The NVDRS summarizes violent death incidents with structured variables and unstructured narratives. We identify 225 latent topics in the narratives (e.g., preparation for death and physical aggression); many of these topics are not captured by existing structured variables. Motivated by known patterns in suicide and homicide by gender and recent research on gender biases in semantic space, we identify the gender bias of our topics (e.g., a topic about pain medication is feminine). We then compare the gender bias of topics to their prevalence in narratives of female versus male victims. Results provide a detailed quantitative picture of reporting about lethal violence and its gendered nature. Our method offers a flexible and broadly applicable approach to model topics in text data.
登录
查看更多内容
DOI：
10.1073/pnas.0307752101
发表时间：
2004-04-06
影响因子：
11.1
作者：
Griffiths, TL;Steyvers, M
通讯作者：
Steyvers, M
DOI：
10.1073/pnas.1720347115
发表时间：
2018-04-17
影响因子：
11.1
作者：
Garg, Nikhil;Schiebinger, Londa;Zou, James
通讯作者：
Zou, James
影响因子：
5.9
作者：
Hemenway, David;Barber, Catherine;Miller, Matthew
通讯作者：
Miller, Matthew
影响因子：
5.6
作者：
Chen, Yi-Xiang
通讯作者：
Chen, Yi-Xiang
影响因子：
1.5
作者：
Fox, James Alan;Fridel, Emma E.
通讯作者：
Fridel, Emma E.