Antoine J.-P. Tixier

GoWvis: A Web Application for Graph-of-Words-based Text Visualization and Summarization.

We introduce GoWvis1, an interactive web application that represents any piece of text inputted by the user as a graph-ofwords and leverages graph degeneracy and community detection to generate an extractive summary (keyphrases and sentences) of the inputted text in an unsupervised fashion. The entire analysis can be fully customized via the tuning of many text preprocessing, graph building, and graph mining parameters. Our system is thus well suited to educational purposes, exploration and early research experiments. The new summarization strategy we propose also shows promise.

A Graph Degeneracy-based Approach to Keyword Extraction

We operate a change of paradigm and hypothesize that keywords are more likely to be found among influential nodes of a graph-ofwords rather than among its nodes high on eigenvector-related centrality measures. To test this hypothesis, we introduce unsupervised techniques that capitalize on graph degeneracy. Our methods strongly and significantly outperform all baselines on two datasets (short and medium size documents), and reach best performance on the third one (long documents).

Construction Safety Risk Modeling and Simulation

By building on a genetic-inspired attribute-based conceptual framework for safety risk analysis, we propose a novel approach to define, model, and simulate univariate and bivariate construction safety risk at the situational level. Our fully data-driven techniques provide construction practitioners and academicians with an easy and automated way of getting valuable empirical insights from attribute-based data extracted from unstructured textual injury reports. By applying our methodology on a data set of 814 injury reports, we first show the frequency-magnitude distribution of construction safety risk to be very similar to that of many natural phenomena such as precipitation or earthquakes. Motivated by this observation, and drawing on state-of-the-art techniques in hydroclimatology and insurance, we then introduce univariate and bivariate nonparametric stochastic safety risk generators based on kernel density estimators and copulas. These generators enable the user to produce large numbers of synthetic safety risk values faithful to the original data, allowing safety-related decision making under uncertainty to be grounded on extensive empirical evidence. One of the implications of our study is that like natural phenomena, construction safety may benefit from being studied quantitatively by leveraging empirical data rather than strictly being approached through a managerial perspective using subjective data, which is the current industry standard. Finally, a side but interesting finding is that in our data set, attributes related to high energy levels (e.g., machinery, hazardous substance) and to human error (e.g., improper security of tools) emerge as strong risk shapers.

Kernel Graph Convolutional Neural Nets

Graph kernels have been successfully applied to many graph classification problems. Typically, a kernel is first designed, and then an SVM classifier is trained based on the features defined implicitly by this kernel. This two-stage approach decouples data representation from learning, which is suboptimal. On the other hand, Convolutional Neural Networks (CNNs) have the capability to learn their own features directly from the raw data during training. Unfortunately, they cannot handle irregular data such as graphs. We address this challenge by using graph kernels to embed meaningful local neighborhoods of the graphs in a continuous vector space. A set of filters is then convolved with these patches, pooled, and the output is then passed to a feedforward network. With limited parameter tuning, our approach outperforms strong baselines on 7 out of 10 benchmark datasets.