Developing and improving computational approaches to covering news can
increase journalistic output and improve the way stories are covered. In this
work we approach the problem of covering crime stories in Los Angeles. We
present a machine-in-the-loop system that covers individual crimes by (1)
learning the prototypical coverage archetypes from classical news articles on
crime to learn their structure and (2) using output from the Los Angeles Police
department to generate "lede paragraphs", first structural unit of
crime-articles. We introduce a probabilistic graphical model for learning
article structure and a rule-based system for generating ledes. We hope our
work can lead to systems that use these components together to form the
skeletons of news articles covering crime.
This work was done for a class project in Jonathan May's Advanced Natural
Language Processing Course, Fall, 2019.

该研究提出了一种机器辅助系统，使用概率图模型和规则系统来生成文章结构和提要段落，以覆盖洛杉矶的犯罪报道，旨在提高新闻报道的效率和准确性。

血红满地，头条报道：计算方法报道洛杉矶的犯罪

If it Bleeds, it Leads: A Computational Approach to Covering Crime in Los Angeles

Real-time crime forecasting is important. However, accurate prediction of
when and where the next crime will happen is difficult. No known physical model
provides a reasonable approximation to such a complex system. Historical crime
data are sparse in both space and time and the signal of interests is weak. In
this work, we first present a proper representation of crime data. We then
adapt the spatial temporal residual network on the well represented data to
predict the distribution of crime in Los Angeles at the scale of hours in
neighborhood-sized parcels. These experiments as well as comparisons with
several existing approaches to prediction demonstrate the superiority of the
proposed model in terms of accuracy. Finally, we present a ternarization
technique to address the resource consumption issue for its deployment in real
world. This work is an extension of our short conference proceeding paper [Wang
et al, Arxiv 1707.03340].

本研究使用空间时间残差网络预测了洛杉矶不同区域在小时层面的犯罪分布情况，通过和其他预测方法的比较证明了其更高的准确度，并提出使用三值化技术解决实际资源短缺的问题。

深度学习实时犯罪预测及其三值化

Deep Learning for Real-Time Crime Forecasting and its Ternarization

Accurate real time crime prediction is a fundamental issue for public safety,
but remains a challenging problem for the scientific community. Crime
occurrences depend on many complex factors. Compared to many predictable
events, crime is sparse. At different spatio-temporal scales, crime
distributions display dramatically different patterns. These distributions are
of very low regularity in both space and time. In this work, we adapt the
state-of-the-art deep learning spatio-temporal predictor, ST-ResNet [Zhang et
al, AAAI, 2017], to collectively predict crime distribution over the Los
Angeles area. Our models are two staged. First, we preprocess the raw crime
data. This includes regularization in both space and time to enhance
predictable signals. Second, we adapt hierarchical structures of residual
convolutional units to train multi-factor crime prediction models. Experiments
over a half year period in Los Angeles reveal highly accurate predictive power
of our models.

研究使用深度学习的 ST-ResNet 预测洛杉矶地区的实时犯罪分布，通过对原始犯罪数据进行空间和时间的正则化处理，采用细节残差卷积单元的层次结构来训练多因素犯罪预测模型，实验证明了该模型具有高度准确的预测能力。