Open Information Extraction: A Review of Baseline Techniques, Approaches, and Applications

With the abundant amount of available online and offline text data, there arises a crucial need to extract the relation between phrases and summarize the main content of each document in a few words. For this purpose, there have been many studies recently in Open Information Extraction (OIE). OIE improves upon relation extraction techniques by analyzing relations across different domains and avoids requiring hand-labeling pre-specified relations in sentences. This paper surveys recent approaches of OIE and its applications on Knowledge Graph (KG), text summarization, and Question Answering (QA). Moreover, the paper describes OIE basis methods in relation extraction. It briefly discusses the main approaches and the pros and cons of each method. Finally, it gives an overview about challenges, open issues, and future work opportunities for OIE, relation extraction, and OIE applications.Comment: 15 pages, 9 figure

Robustness of Fairness in Machine Learning

As machine learning algorithms become widely used in society, certain subgroups are more at risk of being harmed by unfair treatment. Fairness metrics have been proposed to quantify this harm by measuring certain statistics with respect to an evaluation dataset. In this work, we seek to analyze how robust these metrics are. That is, we are interested in whether these metrics give the same ``fairness score'' when measured on different sets of samples from the same distribution. This is important because it gives us insight into how much we can trust the conclusions given by a fairness metric prior to deployment of a model. We design a framework to conduct experiments to test the robustness of a popular fairness metric. We find that, when compared to more traditional performance metrics, it is more sensitive to fluctuations in the evaluation dataset in a variety of settings. Additionally, our work provides a foundation for studying the robustness of fairness metrics in general.NAhttp://deepblue.lib.umich.edu/bitstream/2027.42/176722/1/Honors_Capstone_Fairness_ML_-_Serafina_Kamp.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/176722/2/Honors_Capstone_Fairness_Poster_-_Serafina_Kamp.ppt