Chris Culy's career has spanned a wide range of areas, from mathematical linguistics, to fieldwork in Mali, to theoretical syntax and morphology, to machine translation, to summarization, and most recently to the visualization of linguistic information. He has worked in both academia and industry in a variety of countries. He is currently a visiting professor at the University of Tübingen.

Learning from MOTHs

Ordinary researchers should have access to high level visualizations and analysis tools for their own data. In this talk I present a series of visualizations of language related information that I have developed with that goal in mind, discussing aspects of their design and development, from both pragmatic and theoretical perspectives. In addition, I propose an approach to achieving that goal, which draws on my experiences developing these visualizations as well as ideas and techniques from interaction design and machine learning.

Marti Hearst is a Professor in the School of Information at UC Berkeley, with an affiliate appointment in the Computer Science Division. Her primary research interests are user interfaces for search engines, information visualization, natural language processing, and improving MOOCs. She wrote the first book on Search User Interfaces and did very early work on text visualizations including the TileBars query term visualization and UIs for Scatter/Gather clustering. She has developed numerous search user interfaces including Faceted Navigation via the Flamenco project, the BioText search UI project, the ChaCha Web search interface project, and most recently WordSeer for humanities scholars and social scientists. In 2013 she was elected a Fellow of the ACM.

The HCI Angle on Interactive Language Learning, Visualization, and Interfaces

For this talk, I have been asked to focus on the HCI (human-computer interaction) aspects of interactive language learning, visualization, and interfaces. I have marshaled my thoughts into four main questions: What’s special about visualizing text? Does dimensionality reduction make us dim? How far can we push auto-suggest? and How to support the “middle game” in the search process?

Jimmy Lin is an Associate Professor in the College of Information Studies (The iSchool) at the University of Maryland, with a joint appointment in the Institute for Advanced Computer Studies (UMIACS) and an affiliate appointment in the Department of Computer Science. He graduated with a Ph.D. in Electrical Engineering and Computer Science from MIT in 2004. Lin's research lies at the intersection of information retrieval and natural language processing; his current work focuses on large-scale distributed algorithms and infrastructure for data analytics. From 2010-2012, Lin spent an extended sabbatical at Twitter, where he worked on services designed to surface relevant content to users and analytics infrastructure to support data science.

Tackling Complex Problems with NLP, Big Data, and Visualization

Big data, NLP, and visualization form a powerful combination that promises to deliver compelling applications for tackling a variety of complex problems. NLP exploits rich representations, big data make subtle relationships more obvious, and visualizations allow insights to “leap off the page”. This integration, however, is fraught with challenges stemming from impedance mismatches between various techniques. In this talk, I will discuss these challenges and present some recent attempts in building applications that lie at this intersection.

Noah Smith is the Finmeccanica Associate Professor of Language Technologies and Machine Learning in the School of Computer Science at Carnegie Mellon University. He received his Ph.D. in Computer Science, as a Hertz Foundation Fellow, from Johns Hopkins University in 2006 and his B.S. in Computer Science and B.A. in Linguistics from the University of Maryland in 2001. His research interests include statistical natural language processing, especially unsupervised methods, machine learning for structured data, and applications of natural language processing. His book, Linguistic Structure Prediction, covers many of these topics. He has served on the editorial board of the journals Computational Linguistics (2009–2011), Journal of Artificial Intelligence Research (2011–present), and Transactions of the Association for Computational Linguistics (2012–present) and received a best paper award at the ACL 2009 conference. His research group, Noah's ARK, is currently supported by the NSF (including an NSF CAREER award), DARPA, IARPA, ARO, and gifts from Amazon and Google.

My Adventures in Interdisciplinarity

The explosion of interest in applying natural language processing to new and surprising problems has increased the chance that you, an NLP researcher, will find yourself working on a project with people from other disciplines. In this talk, I'll share my group's experiences of working with people from rather different academic fields (political science, economics, the humanities), including lessons I have learned. Along the way, I'll highlight some examples of the results of those collaborations, though the talk will be mostly non-technical.

Krist is a senior data visualization scientist at Twitter, where he uses information visualization to improve how data scientists, engineers and product managers interact with rich and massive datasets. He enjoys exploring Twitter data and occasionally creates public-facing visualizations (#interactive). Before joining Twitter, he received a PhD in Computer Science from University of Maryland under the supervision of Dr. Ben Shneiderman. His dissertation was focused on temporal data visualization. Two things he like the most are soccer and visualization.

Making Sense of Millions of Thoughts: Finding Patterns in Tweets

Everyday on Twitter, there are millions of thoughts that are captured and shared to the world in the form of 140-character messages, or Tweets. There are many things we could learn from these thoughts if we could figure out a way to digest this gigantic dataset. Visualization is one of the many ways to extract information from these Tweets. In this presentation, I will talk about several visualizations based on Tweets, as well as share experiences and challenges from working with Tweet data.