<html><head></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; "><div>==============================================================</div> First Call for Paper Submissions<br><br> NAACL-HLT 2010 Workshop on<div>Active Learning for Natural Language Processing (ALNLP)<br><br> June 5 or 6, 2010, Los Angeles, CA<br><br> <a href="http://www.active-learning.net/alnlp2010">http://www.active-learning.net/alnlp2010</a><br><br> Submission Deadline: March 1, 2010<br>==============================================================</div><div><br></div><div>Labeled training data is required to achieve state-of-the-art performance for </div><div>many machine learning solutions to NLP tasks. While traditional supervised </div><div>methods rely exclusively on existing labeled data to induce a model, active </div><div>learning allows the learner to select unlabeled data for labeling in an effort to </div><div>reduce annotation costs without sacrificing performance. Thus, active learning </div><div>appears promising for NLP applications where unlabeled data is readily </div><div>available (e.g., web pages, audio recordings, minority language data), but </div><div>obtaining labels is cost-prohibitive.</div><div><br></div><div>Ample recent work has demonstrated the effectiveness of active learning over</div><div>a diverse range of applications. Despite these findings, active learning has </div><div>not yet been widely adopted for many ongoing large-scale corpora annotation </div><div>efforts -- resulting in a dearth of real-world case studies and copious research</div><div>questions. Machine learning literature has primarily focused on active learning </div><div>in the context of classification, devoting less attention to issues specific to NLP </div><div>including annotation user studies, incorporation of semantic information, and</div><div>more complex prediction tasks (e.g. parsing, machine translation).</div><div><br></div><div><br></div><div>TOPICS</div><div><br></div><div>The aim of this workshop is to foster innovation and discussion that advances</div><div>our understanding in these and other practical issues for active learning in NLP.</div><div>Topics of particular interest include:</div><div><br></div><div> -- Alternative query types: labeling features rather than instances, </div><div> mixed-resolution queries for structured instances, etc.</div><div> -- Creative ways for obtaining data via active learning (e.g., online games, </div><div> Mechanical Turk)</div><div> -- Managing multiple, possibly non-expert annotators (e.g., "crowdsourcing" </div><div> environments)</div><div> -- Reusability: using data acquired with one active learner to train other </div><div> model classes</div><div> -- Domain adaptation and active learning</div><div> -- Multi‐task active learning</div><div> -- Criteria for stopping and monitoring active learning progress</div><div> -- Active learning in coordination with semi-supervised or unsupervised </div><div> learning approaches</div><div> -- Interactive active learning interfaces and other HCI issues</div><div> -- Parallelization of active learning and its computational challenges</div><div> -- Software engineering considerations for active learning and NLP</div><div> -- Theoretical analysis of active learning</div><div><br></div><div>We also welcome case-study papers describing the application of active </div><div>learning in real-world annotation projects and lessons learned thereby. </div><div>Additionally, we would consider papers with insights applicable to NLP from </div><div>other machine learning communities (e.g., computer vision, bioinformatics, </div><div>and data mining), where annotation costs are also high. </div><div><br></div><div><br></div><div>SUBMISSIONS</div><div><br></div><div>We invite submissions of two kinds: 1. original and unpublished work as<br>full papers, limited to 8 pages (+1 extra page for references); 2. position or </div><div>work-in-progress papers, limited to 4 pages (including references). Both kinds </div><div>of papers will appear in the proceedings and presented orally. As reviewing </div><div>will be double-blind, author information should not be included in the papers </div><div>and self-reference should be avoided.</div><div><br></div><div>All submissions must be made in PDF format using the START paper </div><div>submission website:<br><a href="https://www.softconf.com/naaclhlt2010/alnlp/">https://www.softconf.com/naaclhlt2010/alnlp/</a><br>Submissions must follow the NAACL HLT 2010 formatting requirements:<br><a href="http://naaclhlt2010.isi.edu/authors.html">http://naaclhlt2010.isi.edu/authors.html</a><br>Authors are strongly encouraged to use the LaTeX or Microsoft Word style<br>files available there. Papers not conforming to these requirements are<br>subject to rejection without review.</div><div><br></div><div><br></div><div>IMPORTANT DATES<br><br>March 1, 2010: Paper Submission Deadline<br>March 30, 2010: Notification of acceptance<br>June 5 or 6, 2010: Workshop held in conjunction with NAACL-HLT<br></div><div><br></div><div><br></div><div>ORGANIZERS AND CONTACT<br><br> - Burr Settles, Carnegie Mellon University, USA<br> - Kevin Small, Tufts University, USA<br> - Katrin Tomanek, University of Jena, Germany<br><br>Please address any queries regarding the workshop to:</div><div> <a href="mailto:alnlp2010@gmail.com">alnlp2010@gmail.com</a><br><br></div><div><br></div><div>PROGRAM COMMITTEE<br><br> - Markus Becker (SPSS, UK)</div><div> - Claire Cardie (Cornell University, USA)</div><div> - Hal Daume III (University of Utah, USA)</div><span style="color: rgb(0, 0, 0); "> - Ben Hachey (University of Edinburgh, UK)</span><br style="color: rgb(0, 0, 0); "><span style="color: rgb(0, 0, 0); "> - Robbie Haertel (Brigham Young University, USA)<br><span style="color: rgb(0, 0, 0); "><span style="color: rgb(0, 0, 0); "> - Udo Hahn (University of Jena, Germany)</span><br> - Eric Horvitz (Microsoft Research, USA)</span><br style="color: rgb(0, 0, 0); "> - Rebecca Hwa (University of Pittsburgh, USA)<br></span><span class="Apple-style-span" style="color: rgb(0, 0, 255); "><span style="color: rgb(0, 0, 0); "><span style="color: rgb(0, 0, 0); "><span style="color: rgb(0, 0, 0); "> - Ashish Kapoor (Microsoft Research, USA)</span><br style="color: rgb(0, 0, 0); "> - Prem Melville (IBM T.J. Watson Research Center, USA)</span><br style="color: rgb(0, 0, 0); "><span style="color: rgb(0, 0, 0); "> - Ray Mooney (University of Texas at Austin, USA)</span><br><span style="color: rgb(0, 0, 0); "> - Fredrik Olsson (SICS, Sweden)</span><br> - Foster Provost (New York University, USA)</span></span><div><span class="Apple-style-span" style="color: rgb(0, 0, 255); "><span style="color: rgb(0, 0, 0); "> - Eric Ringger (Brigham Young University, USA)</span><br style="color: rgb(0, 0, 0); "></span><span style="color: rgb(0, 0, 0); "> - Dan Roth (University of Illinois at Urbana-Champaign, USA)</span><br><div> - Burr Settles (Carnegie Mellon University, USA)</div><span style="color: rgb(0, 0, 0); "><span style="color: rgb(0, 0, 255); "><span style="color: rgb(0, 0, 0); "> - Kevin Small (Tufts University, USA)</span></span><br> - Katrin Tomanek (University of Jena, Germany)<br></span><span style="color: rgb(0, 0, 255); "><span style="color: rgb(0, 0, 0); "><br></span></span></div></body></html>