Open Conference Systems

The more tools that make it easy to create metadata the better it is for catalogers and the people they serve. Open Conference Systems makes it easy as selecting an option to create OAI metadata for harvesting.

Open Conference Systems (OCS) is a free Web publishing tool that will create a complete Web presence for your scholarly conference. OCS will allow you to:

• create a conference Web site
• compose and send a call for papers
• electronically accept paper and abstract submissions
• allow paper submitters to edit their work
• post conference proceedings and papers in a searchable format
• post, if you wish, the original data sets
• register participants
• integrate post-conference online discussions

Genre and Form Terms for Law

Image via WikipediaNews from LC. Since early 2007, the Library of Congress has created over 600 genre/form terms for moving images, sound recordings, and cartographic materials. In November 2010 the Policy and Standards Division (PSD) will approve approximately 80 genre/form terms for law, the culmination of a successful partnership with the American Association of Law Libraries (AALL), whose members developed a thesaurus of law genre/form terms and presented it to PSD. (For AALL's thesaurus see http://www.aallnet.org/sis/tssis/committees/cataloging/classification/genreterms/lawgenreformterms2010final.pdf.)

The law genre/form terms will appear on LC's Tentative Weekly List 44 and be approved on November 3, 2010. The Library of Congress plans to implement the terms in new cataloging in early 2011; a separate announcement will be made when the specific date has been determined.

Additional information on this and other genre/form projects can be found on LC's genre/form web page, http://www.loc.gov/catdir/cpso/genreformgeneral.html. The page includes a timeline, an extensive FAQ, reports, discussion papers, and announcements.

Classification of Named Entities In Wikipedia

Fine Grained Classification of Named Entities In Wikipedia by Maksim Tkachenko, Alexander Ulanov, and Andrey Simanovsky has been published as HPL-2010-166.

This report describes the study on classifying Wikipedia articles into an extended set of named entity classes. We employed semi-automatic method to extend Wikipedia class annotation and created a training set for 15 named entity classes. We implemented two classifiers. A binary named-entity classifier decides between articles about named entities and other articles. A support vector machine (SVM) classifier trained on a variety of Wikipedia features determines the class of a named entity. Combination of the two classifiers helped us to boost classification quality and obtain classification quality that is better than state of the art.

Pretty technical, but anything that helps disambiguation sounds fine to me.