The 29th UW/MS Symposium in Computational Linguistics
Microsoft Research and University of Washington
Microsoft, Building 99, 1919
14820 NE 36th Street
98052, United States
3:30 PM – 5 PM
Come take advantage of this opportunity to connect with the computational linguistics community at Microsoft and the University of Washington. This is a regular opportunity for computational linguists at the University of Washington and at Microsoft to discuss topics in the field and to connect in a friendly informal atmosphere. We will have two talks (see below), followed by informal mingling.
Title: Learning with Weak Supervision in Grounded Language Acquisition
Speaker: Hannaneh Hajishirzi
A central problem in grounded language acquisition is to learn the correspondences between a rich set of events and complex sentences that describe those events. In this talk, I will introduce a novel approach to learn these correspondences under weak supervision that comes from loose temporal alignments between events and sentences. The core idea is to exploit the underlying structure between correct, but latent, correspondences using a discriminative notion of similarity coupled with a ranking function.This algorithm reasons in terms of pairwise discriminative similarities and utilizes popularity metrics to learn the alignments between events and sentences and even discover group of events, called macro-events, that best describe a sentence. I will demonstrate extensive evaluations on our new dataset of professional soccer commentaries. Furthermore, I will describe how this model can be applied under the general framework of Multiple Instance Learning.
Hannaneh Hajishirzi has recently joined University of Washington as a research scientist. Prior to that, she spent a year as a postdoctoral research associate at Carnegie Mellon University and Disney Research. She received her PhD in 2011 from the Computer Science department at the University of Illinois at Urbana-Champaign. Her research interests are in Artificial Intelligence and Machine Learning, and their intersections with Natural Language Processing and Information Extraction.
In particular, her current research is mainly focused on semantic analysis of natural language texts and designing automatic language-based interactive systems. Her prior research was on designing statistical relational frameworks to learn, control, and reason about complex dynamic domains such as commentaries, narratives, and web-pages with applications in narrative understanding, web monitoring, spam detection, and near duplicate detection.
Title: Mining Online Social Behavior for Enhanced Behavioral Health
Speaker: Munmun De Choudhury
People are increasingly taking on to social media platforms, as Twitter and Facebook, to share their thoughts and opinions with their audiences. Often these updates are made in a naturalistic setting over the course of daily activities and happenings. Beyond elucidating core aspects of how we act, interact or emote, these platforms thus provide a promising mechanism to capture behavioral attributes relating to an individual’s social and psychological environment, some of which may signal concerns about their mental health.
In this talk, we will examine the harnessing of social media as a tool in behavioral health. Today affective disorders constitute a serious challenge in public health: More than 9% of US population is known to suffer from depression. I will present two problems where social media behavior can help us reveal latent affective concerns. First, I will discuss the use of social media, particularly online activity, emotion and linguistic expression, in making inferences about behavioral changes in new mothers following childbirth. Second, I will present predictive models that leverage social media behavioral cues, to detect, ahead of onset, the likelihood of major depression in individuals. Broadly, such predictive forecasting can help develop unobtrusive diagnostic measures of behavioral disorders, and can hopefully enable behavioral health tracking and surveillance in large populations in a fine-grained manner. I will conclude with the potential of this line of research in informing the design of next generation low-cost, privacy-sensitive early-warning systems and interventions, that can bring people timely information and assistance.
Munmun De Choudhury is a postdoctoral researcher at Microsoft Research, Redmond. Her research interests are in computational social science. By combining data mining, human computer interaction, and social science, Munmun’s research attempts to decipher human behavior, as manifested in people’s online activities. She has been a recipient of the Grace Hopper Scholarship, a finalist of Facebook Fellowship, and winner of two Best Paper Honorable Mention awards from ACM SIGCHI. Earlier, Munmun was a research fellow at Rutgers University, and obtained a PhD in Computer Science from Arizona State University in 2011.