https://www.hlt.inesc-id.pt/wiki/index.php?title=Learning_with_Rich_Prior_Knowledge&feed=atom&action=history
Learning with Rich Prior Knowledge - Revision history
2024-03-29T09:28:46Z
Revision history for this page on the wiki
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https://www.hlt.inesc-id.pt/wiki/index.php?title=Learning_with_Rich_Prior_Knowledge&diff=6230&oldid=prev
Acbm at 11:02, 24 May 2011
2011-05-24T11:02:23Z
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<td colspan="2" style="background-color: #fff; color: #202122; text-align: center;">Revision as of 11:02, 24 May 2011</td>
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<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== Abstract ==</div></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><div>== Abstract ==</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>We possess a wealth of prior knowledge about most prediction problems, and particularly so for many of the fundamental tasks in <del style="font-weight: bold; text-decoration: none;">speech </del>processing <del style="font-weight: bold; text-decoration: none;">and generation. For example, when learning letter to sound rules, even unlabeled words should obey some phonotactic constraints (such as “each word should contain a vowel”)</del>. Unfortunately, it is often difficult to make use of this type of information during learning, as it typically does not come in the form of labeled examples, may be difficult to encode as a prior on parameters in a Bayesian setting, and may be impossible to incorporate into a tractable model. Instead, we usually have prior knowledge about the values of output variables. </div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>We possess a wealth of prior knowledge about most prediction problems,</div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>and particularly so for many of the fundamental tasks in <ins style="font-weight: bold; text-decoration: none;">natural</ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">language </ins>processing. Unfortunately, it is often difficult to make</div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>use of this type of information during learning, as it typically does</div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>not come in the form of labeled examples, may be difficult to encode</div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>as a prior on parameters in a Bayesian setting, and may be impossible</div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>to incorporate into a tractable model. Instead, we usually have prior</div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>knowledge about the values of output variables. <ins style="font-weight: bold; text-decoration: none;">For example, linguistic</ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">knowledge or an out-of-domain parser may provide the locations of</ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">likely syntactic dependencies for grammar induction. Motivated by</ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">the prospect of being able to naturally leverage such knowledge, four</ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">different groups have recently developed similar, general frameworks</ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">for expressing and learning with side information about output variables.</ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">These frameworks are Constraint-Driven Learning (UIUC), Posterior</ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">Regularization (UPenn), Generalized Expectation Criteria (UMass Amherst),</ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">and Learning from Measurements (UC Berkley).</ins></div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del style="font-weight: bold; text-decoration: none;">For example</del>, <del style="font-weight: bold; text-decoration: none;">we might know that several different speech recognizers should learn </del>to <del style="font-weight: bold; text-decoration: none;">agree on untranscribed data</del>. <del style="font-weight: bold; text-decoration: none;">Motivated by </del>the <del style="font-weight: bold; text-decoration: none;">prospect </del>of <del style="font-weight: bold; text-decoration: none;">being able to naturally leverage such knowledge</del>, <del style="font-weight: bold; text-decoration: none;">four different groups have recently developed similar</del>, <del style="font-weight: bold; text-decoration: none;">general frameworks for expressing </del>and learning <del style="font-weight: bold; text-decoration: none;">with side </del>information about <del style="font-weight: bold; text-decoration: none;">output variables</del>. These <del style="font-weight: bold; text-decoration: none;">frameworks are Constraint</del>-<del style="font-weight: bold; text-decoration: none;">Driven Learning (UIUC)</del>, <del style="font-weight: bold; text-decoration: none;">Posterior Regularization (UPenn)</del>, <del style="font-weight: bold; text-decoration: none;">Generalized Expectation Criteria (UMass Amherst)</del>, and <del style="font-weight: bold; text-decoration: none;">Learning from Measurements (UC Berkley)</del>. </div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">This tutorial describes how to encode side information about output</ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">variables</ins>, <ins style="font-weight: bold; text-decoration: none;">and how </ins>to <ins style="font-weight: bold; text-decoration: none;">leverage this encoding and an unannotated</ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">corpus during learning</ins>. <ins style="font-weight: bold; text-decoration: none;">We survey </ins>the <ins style="font-weight: bold; text-decoration: none;">different frameworks, explaining</ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">how they are connected and the trade-offs between them. We also survey</ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">several applications that have been explored in the literature,</ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">including applications to grammar and part-</ins>of<ins style="font-weight: bold; text-decoration: none;">-speech induction, word</ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">alignment, information extraction</ins>, <ins style="font-weight: bold; text-decoration: none;">text classification</ins>, and <ins style="font-weight: bold; text-decoration: none;">multi-view</ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>learning<ins style="font-weight: bold; text-decoration: none;">. Prior knowledge used in these applications ranges from</ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">structural </ins>information <ins style="font-weight: bold; text-decoration: none;">that cannot be efficiently encoded in the model,</ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">to knowledge </ins>about <ins style="font-weight: bold; text-decoration: none;">the approximate expectations of some features, to</ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">knowledge of some incomplete and noisy labellings</ins>. These <ins style="font-weight: bold; text-decoration: none;">applications</ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">also address several different problem settings, including unsupervised,</ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">lightly supervised, and semi</ins>-<ins style="font-weight: bold; text-decoration: none;">supervised learning</ins>, <ins style="font-weight: bold; text-decoration: none;">and utilize both</ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">generative and discriminative models. The diversity of tasks</ins>, <ins style="font-weight: bold; text-decoration: none;">types of</ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">prior knowledge, and problem settings explored demonstrate the generality</ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">of these approaches</ins>, and <ins style="font-weight: bold; text-decoration: none;">suggest that they will become an important tool</ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">for researchers in natural language processing</ins>.</div></td></tr>
<tr><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td><td class="diff-marker"></td><td style="background-color: #f8f9fa; color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #eaecf0; vertical-align: top; white-space: pre-wrap;"><br></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div><del style="font-weight: bold; text-decoration: none;">This tutorial describes how to encode side information about output variables, and how to leverage this encoding and an unannotated corpus during learning. We survey the different frameworks, explaining how they are connected and the trade-offs between them. We also survey some applications to natural language processing that have been explored in the literature including: grammar and part of speech induction, word alignment, information extraction, text classification, and multi-view learning. Prior knowledge used in these applications ranges from structural information that cannot be efficiently encoded in the model, to knowledge about the approximate expectations of some features, to knowledge of some incomplete and noisy labellings. These applications show that a wide variety of domain knowledge can be used to guide unsupervised or semi-supervised learning. The variety of applications and diversity of prior knowledge employed so far demonstrates the generality of these approaches. While previous applications have been in language processing, there are numerous opportunities for applying the frameworks to speech processing, and we highlight several of them.</del></div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>The tutorial will provide the audience with the theoretical background to</div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div> </div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>understand why these methods have been so effective, as well as practical</div></td></tr>
<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div>The tutorial will provide the audience with the theoretical background to understand why these methods have been so effective, as well as practical guidance on how to apply them. Specifically, we discuss issues that come up in implementation, and describe a toolkit that provides <del style="font-weight: bold; text-decoration: none;">“out</del>-of-the-<del style="font-weight: bold; text-decoration: none;">box” </del>support for the applications described in the tutorial, and is extensible to other applications and new types of prior knowledge.</div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>guidance on how to apply them. Specifically, we discuss issues that come</div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>up in implementation, and describe a toolkit that provides <ins style="font-weight: bold; text-decoration: none;">"out</ins>-of-the-<ins style="font-weight: bold; text-decoration: none;">box"</ins></div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>support for the applications described in the tutorial, and is extensible</div></td></tr>
<tr><td colspan="2" class="diff-side-deleted"></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div>to other applications and new types of prior knowledge.</div></td></tr>
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Acbm
https://www.hlt.inesc-id.pt/wiki/index.php?title=Learning_with_Rich_Prior_Knowledge&diff=6228&oldid=prev
Acbm at 14:38, 19 May 2011
2011-05-19T14:38:53Z
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<tr><td class="diff-marker" data-marker="−"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;"><div> </div></td><td class="diff-marker" data-marker="+"></td><td style="color: #202122; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">'''Note:''' This seminar will be held in English.</ins></div></td></tr>
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Acbm
https://www.hlt.inesc-id.pt/wiki/index.php?title=Learning_with_Rich_Prior_Knowledge&diff=6226&oldid=prev
Acbm at 14:37, 19 May 2011
2011-05-19T14:37:57Z
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<p><b>New page</b></p><div>__NOTOC__<br />
{{infobox|name=João Graça<br />
|username=javg<br />
|contact=javg<br />
|phone=+351-213-100-313<br />
|fax=+351-213-145-843<br />
}}<br />
<br />
== Date ==<br />
<br />
* 14:30, Friday, May 27<sup>th</sup>, 2011<br />
* Room 336<br />
<br />
== Speaker ==<br />
<br />
* [[João Graça]]<br />
<br />
== Abstract ==<br />
<br />
We possess a wealth of prior knowledge about most prediction problems, and particularly so for many of the fundamental tasks in speech processing and generation. For example, when learning letter to sound rules, even unlabeled words should obey some phonotactic constraints (such as “each word should contain a vowel”). Unfortunately, it is often difficult to make use of this type of information during learning, as it typically does not come in the form of labeled examples, may be difficult to encode as a prior on parameters in a Bayesian setting, and may be impossible to incorporate into a tractable model. Instead, we usually have prior knowledge about the values of output variables. <br />
<br />
For example, we might know that several different speech recognizers should learn to agree on untranscribed data. Motivated by the prospect of being able to naturally leverage such knowledge, four different groups have recently developed similar, general frameworks for expressing and learning with side information about output variables. These frameworks are Constraint-Driven Learning (UIUC), Posterior Regularization (UPenn), Generalized Expectation Criteria (UMass Amherst), and Learning from Measurements (UC Berkley). <br />
<br />
This tutorial describes how to encode side information about output variables, and how to leverage this encoding and an unannotated corpus during learning. We survey the different frameworks, explaining how they are connected and the trade-offs between them. We also survey some applications to natural language processing that have been explored in the literature including: grammar and part of speech induction, word alignment, information extraction, text classification, and multi-view learning. Prior knowledge used in these applications ranges from structural information that cannot be efficiently encoded in the model, to knowledge about the approximate expectations of some features, to knowledge of some incomplete and noisy labellings. These applications show that a wide variety of domain knowledge can be used to guide unsupervised or semi-supervised learning. The variety of applications and diversity of prior knowledge employed so far demonstrates the generality of these approaches. While previous applications have been in language processing, there are numerous opportunities for applying the frameworks to speech processing, and we highlight several of them.<br />
<br />
The tutorial will provide the audience with the theoretical background to understand why these methods have been so effective, as well as practical guidance on how to apply them. Specifically, we discuss issues that come up in implementation, and describe a toolkit that provides “out-of-the-box” support for the applications described in the tutorial, and is extensible to other applications and new types of prior knowledge.<br />
<br />
<br />
<br />
<br />
<br />
<br />
[[category:Seminars]]<br />
[[category:Seminars 2011]]</div>
Acbm