Goldwasser receives CAREER Award
Social media is the primary space for public sharing of perspectives and conversations. Elected officials and policymakers also use this space to promote decisions and inform the public on projects they care about and where their focus is going next. This massive and growing amount of opinion data is like gold hidden in the hills - knowing it is there doesn’t provide wealth, but efficiently and precisely mining it will derive fascinating new kinds of knowledge - knowledge that can better guide policy makers in the development of effectual new laws, or public officials in timely and relevant aid in response to natural disasters.
Computer science assistant professor and machine learning researcher, Dan Goldwasser, studies the combination of natural language and its social context to provide such guidance from the derived meaning of what is written.
The addition of contextual information to natural language processing (NLP), where meaning is drawn computationally from text, adds important elements of understanding regarding opinion that can provide reliable guidance to those seeking to know trends and positions within a population.
Prof. Goldwasser has received a CAREER award from the National Science Foundation (NSF) to pursue this research direction by developing novel modeling techniques and learning algorithms for combining linguistic content and its necessary social context under a common innovative principle - creating a socially grounded language representation that views opinion understanding as part of a larger framework of understanding real-world scenarios and their participants. This new way to conceptualize opinionated text analysis will produce highly nuanced analysis of social media that captures the stances, attitudes, and relationships between the different stakeholders of a given real-world scenario.
Specifically, Goldwasser’s new project suggests a new way to conceptualize opinionated text analysis, as part of a real-world scenario, reflecting the attitudes-of and relationships-between stakeholders in the scenario from which the text emerges. A major design goal of the research is to avoid the supervision bottleneck, and allow the NLP system to easily adapt to new events and policy issues by using the social information associated with users as a form of indirect supervision over documents they author. This is done by representing documents, authors, referenced entities, their connections and behaviors in a shared neuro-symbolic framework enabling symbolic inference over latent entity representations learned from data.
The project addresses three main challenges. First, it constructs a representation language for characterizing opinions, their targets and motivation, and the stances they express. Second, it grounds opinion text in real world scenarios by infusing relevant real-world information into a neural language model. Third, it exploits social information by formulating a unified view of social, behavioral, and textual information. These research efforts help provide nuanced insights from social media content that lacks specificity on its own, while building the computational foundations for jointly processing textual and social information.
NSF CAREER awards are the organization’s most prestigious awards given to junior faculty who embody the role of teacher-scholars through research, education and the integration of those concepts within the mission of their organizations. CAREER awards support promising and talented researchers in building a foundation for a lifetime of leadership. Receiving this award reflects this project’s merit of the NSF statutory mission and its worthiness of financial support.
Professor Goldwasser is an assistant professor at Purdue’s Department of Computer Science and his research is in artificial intelligence, focusing on machine learning and natural language processing. He is broadly interested in connecting natural language with real world scenarios and using them to guide natural language understanding. His recent work evaluated frameworks for specifying deep relational models, designed to support a variety of NLP scenarios. Prior to joining Purdue University, Goldwasser received his PhD in Computer Science from University of Illinois at Urbana-Champaign and was a postdoctoral researcher at the University of Maryland in College Park.