CALL FOR PAPERS
``Text Learning: Beyond Supervision''

Due to the rapidly increasing amount of textual data available and the range of interesting and important problems arising in text analysis, there has been growing interest in applying machine learning methods to text. There has also been significant recent interest in research that combines supervised and unsupervised learning. This research is especially relevant to text learning because the inherent complexity of natural language usually requires models with many parameters: estimating those parameters requires a lot of data, and obtaining labeled data is difficult and expensive. By combining unsupervised learning with supervised learning, the need for labeled training data can often be greatly reduced, allowing for the development of more powerful models and methods.

The workshop will explore machine learning methods for solving supervised text learning tasks that exploit training methods beyond straightforward supervised learning.

Relevant topics include:

• Text classification with labeled and unlabeled data: EM, co-training, transduction with SVMs, discriminative maximum entropy with unlabeled data.
• Information extraction with unlabeled data: bootstrapping, co-boosting.
• Part-of-speech tagging, parsing and other NLP with unlabeled data.
• Active learning: uncertainty sampling, query-by-committee, version space reduction.
• Model selection with unlabeled data.
• Word clustering for language modeling, distributional clustering, feature generation by clustering.
• Semi-supervised clustering, document clustering with user-feedback.
• Integrating prior knowledge into supervised learning, integrating distantly labeled data.
• Unsupervised methods for learning segmentation models, translation models, lexicons, etc.
• Link analysis for supervised learning.

The last several years have seen many disparate innovations in the intersection between text learning and the combination of supervised and unsupervised learning. The goal of this workshop is to bring together researchers from the machine learning, information retrieval, computational linguistics and language modeling to talk about their different perspectives on this intersection and to share their latest ideas.

Schedule and Papers

Invited Speakers

Tommi Jaakkola
Assistant Professor
Department of Computer Science
Massachusetts Institute of Technology

Important Dates

Submission Procedure

Formatting instructions can be found here.

Organizing Committee

Andrew McCallum
  WhizBang Labs - Research, 4616 Henry Street, Pittsburgh, PA  15218
  T: 412-683-9132 F: 412-683-4436
  mccallum@whizbang.com
  http://www.cs.cmu.edu/~mccallum

Kamal Nigam
  School of Computer Science, Carnegie Mellon, Pittsburgh, PA 15213
  T: 412-268-3070 F: 412-268-5576
  knigam@cs.cmu.edu
  http://www.cs.cmu.edu/~knigam

Tony Jebara
  MIT, Media Laboratory - 20 Ames Street, E15-390, Cambridge, MA 02138
  T: 617-253-0326 F: 617-253-8874
  jebara@media.mit.edu
  http://www.media.mit.edu/~jebara

Lillian Lee
  Department of Computer Science, Cornell University, Ithaca, NY 14853
  T: 607-255-8119 F: 607-255-4428
  llee@cs.cornell.edu
  http://www.cs.cornell.edu/home/llee