The Elsevier Grand Challenge: Knowledge Enhancement in the Life Sciences is a contest created to improve the way scientific information is communicated and used. The contest invites members of the scientific community to describe and prototype a tool to improve the interpretation and identification of meaning in (online) journals and text databases relating to the life sciences. Specifically we are looking for new ways to:
mendation service which can be called via HTTP by BibSonomy's recommender when a user posts a bookmark or publication. All participating recommenders are called on each posting process, one of them is choosen to actually deliver the results to the user. We can then measure
I should have realized the danger of stepping into the Wikipedia morass, and the comments on today’s earlier post further indicate my folly in doing so. You know, The New York Times gets things wrong, too. As an argument on a sophisticated level, it’s that all texts are constructs reflecting the attitude of the constructor rather than a verifiable external reality; on a less sophisticated level, it’s that all the other kids are smoking pot, too.
I’ve had enough. I’m bringing it down to this challenge.
he Diagnostic Competition is proposed to be the first of a series of international competitions that will be hosted yearly at the International Workshop on Principles of Diagnosis (DX).
mendation service which can be called via HTTP by BibSonomy's recommender when a user posts a bookmark or publication. All participating recommenders are called on each posting process, one of them is choosen to actually deliver the results to the user. We can then measure
Our main goal is to provide you with data because you know what you want to do with it. Still, we give some information regarding typical MIR tasks below. We hope to provide snippets of code and benchmarks results to help you getting started. If you want to provide additional information / link to your code / new results / new tasks, please send us an email! We also try to maintain an informal list of publications that use the dataset.
This research paper explains how increasing and improving practitioners’ knowledge of the importance and value of speech, language and communication skills contributes to advancement of educational, social and emotional competences; focus was on development for children in the Early Years. Proposed is the necessity to embed speech, language and communication development in practice, and the provision of a language and communication rich environment is considered a key strategy to influencing progress. The paper describes a research project that was subsequently evaluated using a multiple-method approach to afford a comprehensive analysis of findings. Outcomes were to highlight necessity for improvement of knowledge of less experienced practitioners, and added reinforcement for those who were relatively proficient; further, it was suggested that effective mentoring was required to maintain wide-ranging and continual growth of practitioners’ expertise. Development of confidence in subject knowledge was also essential in providing a child-initiated approach to learning; this, it claims, would enhance the fostering of a learning community which would place greater importance on the requirement for enhancement of speech, language and communication skills.
This year's discovery challenge presents two tasks in the new area of social bookmarking. One task covers spam detection and the other covers tag recommendations. As we are hosting the social bookmark and publication sharing system BibSonomy, we are able to provide a dataset of BibSonomy for the challenge. A training dataset for both tasks is provided at the beginning of the competition.
The test dataset will be released 48 hours before the final deadline. Due to a very tight schedule we cannot grant any deadline extension.
The presentation of the results will take place at the ECML/PKDD workshop where the top teams are invited to present their approaches and results.
Kaggle is a platform for data prediction competitions. Companies, organizations and researchers post their data and have it scrutinized by the world's best statisticians.
I. Zaihrayeu, L. Sun, F. Giunchiglia, W. Pan, Q. Ju, M. Chi, and X. Huang. Proceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Korea, volume 4825 of LNCS, page 617--630. Berlin, Heidelberg, Springer Verlag, (November 2007)
J. Yamagishi, T. Nose, H. Zen, T. Toda, and K. Tokuda. Proceedings of the 2008 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), page 3957-3960. Las Vegas, NV, USA, (March 2008)
L. Ratinov, and D. Roth. Proceedings of the Thirteenth Conference on Computational Natural Language Learning, page 147--155. Stroudsburg, PA, USA, Association for Computational Linguistics, (2009)