IQSS Events Organizer

New Directions in Text Analysis

Thursday, May 28, 2009 - Friday, May 29, 2009

Description: This conference, sponsored by the Eric M. Mindich Conference on Experimental Social Science, will bring together a group of approximately 45 scholars in the social sciences, biomedical sciences, humanities, and law ("substantive" researchers), along with methodological innovators in
computer science, computational linguistics, and statistics ("technical" researchers). Specifically, the conference aims to a) provide substantive researchers with a better idea of cutting edge methods for natural language processing, b) provide technical researchers a better sense of the fundamental questions that drive much empirical work in the humanities, social sciences, law, and biomedical sciences and c) identify points of intersection between important substantive problems and open methodological problems.

Fri May 29, 2009

8:15am – 8:45am: Coffee and Pastries
Location: Belfer Case Study Room S020 1737 Cambridge Street Cambridge MA 02138 map

8:45am – 9:00am

Welcome and Introduction

Location

1737 Cambridge Street Cambridge MA 02138 map

Moderator

Peter Bol (Harvard University)
Kevin Quinn (Harvard University)
Stuart M Shieber (Harvard University)

9:00am – 9:45am

Location

1737 Cambridge Street Cambridge MA 02138 map

Presentations

Chris Manning (Stanford University)

“Probabalistic Explanation in Linguistics”

9:50am – 10:35am

Location

1737 Cambridge Street Cambridge MA 02138 map

Presentations

Greg Crane (Tufts University)
David Bamman (Tufts University)

“No Unmediated Interpretation: The Mature Life Cycle of Detection, Classification, and Creation”

10:35am – 10:45am: Coffee Break
Location: 1737 Cambridge Street Cambridge MA 02138 map

10:45am – 11:30am

Location

1737 Cambridge Street Cambridge MA 02138 map

Presentations

Philip Resnik (University of Maryland, College Park)

“The Spirit is Willing, but the Meat is Still Rotten: Why Machinde Translation Remains a Challenge, and What We're Doing About It”

11:35am – 12:20pm

Location

1737 Cambridge Street Cambridge MA 02138 map

Presentations

Regina Barzilay (MIT)

“Embracing Language Diversity: Unsupervised Multilingual Learning”

12:20pm – 1:00pm: Lunch
Location: 1737 Cambridge Street Cambridge MA 02138 map

1:00pm – 1:45pm

Location

1737 Cambridge Street Cambridge MA 02138 map

Presentations

David Blei (Princeton University)

“Topic Models: A Computational Approach to Organizing, Exploring, and Understanding Scholarly Archives”

1:50pm – 2:35pm

Location

1737 Cambridge Street Cambridge MA 02138 map

Presentations

Andrew McCallum (UMass Amherst)

“Unified Models of Text and Metadata with Application to Poltical, Legal, and Bibliometric Data”

2:35pm – 2:45pm: Coffee Break
Location: 1737 Cambridge Street Cambridge MA 02138 map

2:45pm – 3:30pm

Location

1737 Cambridge Street Cambridge MA 02138 map

Presentations

Peter Bol (Harvard University)
Stuart Shieber (Harvard University)
Rani Nelken (Harvard University)

“Mining Chinese Texts for Bibliographical Data”

3:45pm – 4:20pm

Location

1737 Cambridge Street Cambridge MA 02138 map

Presentations

Ozlem Uzuner (State University of New York)

“Natural Language Processing on Clinical Records”

4:25pm – 5:10pm

Location

1737 Cambridge Street Cambridge MA 02138 map

Presentations

David Mimno (University of Massachusetts, Amherst)

“Text Mining for Humanistic Scholarship”

abstract

Topic and Role Discovery in Social Networks with Experiments on Enron and Academic Email. Andrew McCallum, Xuerui Wang and Andres Corrada-Emmanuel. Journal of Artificial Intelligence Research (JAIR), 2007. (Journal paper version of IJCAI conference paper on Author-Recipient-Topic (ART) model.)

Topics over Time: A Non-Markov Continuous-Time Model of Topical Trends. Xuerui Wang and Andrew McCallum. Conference on Knowledge Discovery and Data Mining (KDD) 2006. (A LDA-style topic model that models trends over time. The meaning of a topic remains fixed and reliable, but its prevalence over time is captured, and topics may thus focus in on co-occurrence patterns that are time-sensitive. Unlike other work that relies on Markov assumptions or discretization of time, here each topic is associated with a continuous distribution over timestamps. Improvements in topic saliency and the ability to predict time given words.)

Group and Topic Discovery from Relations and Their Attributes. Xuerui Wang, Natasha Mohanty and Andrew McCallum. Neural Informaion Processing Systems (NIPS), 2006. (Social network analysis that simultaneously discovers groups of entities and also clusters attributes of their relations, such that clustering in each dimension informs the other. Applied to the voting records and corresponding text of resolutions from the U.S. Senate and the U.N., showing that incorporating the votes results in more salient topic clusters, and that different groupings of legislators emerge from different topics.)

Joint Group and Topic Discovery from Relations and Text. Andrew McCallum, Xuerui Wang and Natasha Mohanty, Statistical Network Analysis: Models, Issues and New Directions, Lecture Notes in Computer Science 4503, pp. 28-44, (Book chapter), 2007. (Book chapter version of NIPS 2006 conference paper. Social network analysis that simultaneously discovers groups of entities and also clusters attributes of their relations, such that clustering in each dimension in forms the other. Applied to the voting records and corresponding text of resolutions from the U.S. Senate and the U.N., showing that incorporating the votes results in more salient topic clusters, and that different groupings of legislators emerge from different topics.)

Topic Models Conditioned on Arbitrary Features with Dirichlet-multinomial Regression. David Mimno and Andrew McCallum. (Plenary presentation.) Conference on Uncertainty in Artificial Intelligence, (UAI), 2008. (Text documents are usually accompanied by metadata, such as the authors, the publication venue, the date, and any references. Work in topic modeling that has taken such information into account, such as Author-Topic, Citation-Topic, and Topic-over-Time models, has generally focused on constructing specific models that are suited only for one particular type of metadata. This paper presents a simple, unified model for learning topics from documents given arbitrary non-textual features, which can be discrete, categorical, or continuous.)

Community-based Link Prediction with Text. David Mimno, Hanna M. Wallach and Andrew McCallum. In Proceedings of the NIPS 2007 Workshop on Statistical Network Modeling (NIPS WS), 2007. (New state-of-the-art results in link-prediction using a latent-variable topic model, in which "community" variables are associated with topic distributions and author distributions. Thus the model combines the use of language/topics and co-authorships to discover communities.)

Expertise Modeling for Matching Papers with Reviewers. David Mimno and Andrew McCallum. Conference on Knowledge Discovery and Data Mining (KDD), 2007. (The Author-Persona-Topic model is a LDA-style topic model especially designed to represent expertise as a mixture of topical intersections. We show positive results in matching reviewers to conference papers, as assessed by human judgements.)

Organizing the OCA: Learning faceted subjects from a library of digital books. David Mimno and Andrew McCallum. Joint Conference on Digital Libraries (JCDL), 2007. (Introduces the DCM-LDA topic model, which represents topics by a Dirichlet-compound-multinomial rather than a multinomial. In addition to obtaining interesting information about the difference varianes of the topics, this model lends itself to efficient parallelization with very coarse-grained synchronization. The result is a topic model that can run on over 1 billion words in just a few hours.)

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