Steven JM Jones
Also published as:
Understanding who uses Reddit: Profiling individuals with a self-reported bipolar disorder diagnosis
Proceedings of the Seventh Workshop on Computational Linguistics and Clinical Psychology: Improving Access
Recently, research on mental health conditions using public online data, including Reddit, has surged in NLP and health research but has not reported user characteristics, which are important to judge generalisability of findings. This paper shows how existing NLP methods can yield information on clinical, demographic, and identity characteristics of almost 20K Reddit users who self-report a bipolar disorder diagnosis. This population consists of slightly more feminine- than masculine-gendered mainly young or middle-aged US-based adults who often report additional mental health diagnoses, which is compared with general Reddit statistics and epidemiological studies. Additionally, this paper carefully evaluates all methods and discusses ethical issues.
Painless Relation Extraction with Kindred
Relation extraction methods are essential for creating robust text mining tools to help researchers find useful knowledge in the vast published literature. Easy-to-use and generalizable methods are needed to encourage an ecosystem in which researchers can easily use shared resources and build upon each others’ methods. We present the Kindred Python package for relation extraction. It builds upon methods from the most successful tools in the recent BioNLP Shared Task to predict high-quality predictions with low computational cost. It also integrates with PubAnnotation, PubTator, and BioNLP Shared Task data in order to allow easy development and application of relation extraction models.
VERSE: Event and Relation Extraction in the BioNLP 2016 Shared Task
Steven JM Jones
Proceedings of the 4th BioNLP Shared Task Workshop