Reading Graphs, Maps, Trees as a late-stage graduate student in 2008 was invigorating.1 Here was an approach to literary history free from the pieties of “close reading,” committed to empiricism, seeking to fulfill the promise of the sociology of literature with its “materialist conception of form” (92). And, at the time, it seemed entirely natural that the way to follow the path laid out by Fr...

This paper describes our system to extract binary regulatory relations from text, used to participate in the SeeDev task of BioNLP-ST 2016. Our system was based on machine learning, using support vector machines with a shallow linguistic kernel to identify each type of relation. Additionally, we employed a distant supervised approach to increase the size of the training data. Our submission obt...

This paper describes the use of a hardware and software environment that allows distant access to RF electronics measurement devices called Remote RF Laboratory. This access only requires conventional Internet browser and corresponding plug-in, both available for free, and low speed Internet connexion. Remote users may perform web-based RF measurements at any time, from any place, which is part...

The majority of research on extracting missing user attributes from social media profiles use costly hand-annotated labels for supervised learning. Distantly supervised methods exist, although these generally rely on knowledge gathered using external sources. This paper demonstrates the effectiveness of gathering distant labels for self-reported gender on Twitter using simple queries. We confir...

Relation extraction has benefited from distant supervision in recent years with the development of natural language processing techniques and data explosion. However, distant supervision is still greatly limited by the quality of training data, due to its natural motivation for greatly reducing the heavy cost of data annotation. In this paper, we construct an architecture called MIML-sort (Mult...

Biological pathways are central to understanding complex diseases such as cancer. The majority of this knowledge is scattered in the vast and rapidly growing research literature. To automate knowledge extraction, machine learning approaches typically require annotated examples, which are expensive and time-consuming to acquire. Recently, there has been increasing interest in leveraging database...