I'm interested in machine learning techniques (graphical models, kernel methods) applied to text understanding (entity and relation extraction, coreference resolution, document classification and clustering, confidence prediction, social network analysis, data mining).
The mission of the Journal of Machine Learning Gossip (JMLG) is to provide an archival source of important information that is often discussed informally at conferences but is rarely, if ever, written down.
What's Torch ?
It's a machine-learning library, written in simple C++ and distributed now under a BSD license.
Torch is currently developed at IDIAP, in Switzerland mountains.
Lush is an object-oriented programming language designed for researchers, experimenters, and engineers interested in large-scale numerical and graphic applications.
The aim of MLpedia is to provide comprehensive information on a range of machine learning methods and applications, written and maintained by researchers and practitioners. Find out how to take part.
Neil Ireson, Fabio Ciravegna, Marie Elaine Califf, Dayne Freitag, Nicholas Kushmerick, Alberto Lavelli: Evaluating Machine Learning for Information Extraction, 22nd International Conference on Machine Learning (ICML 2005), Bonn, Germany, 7-11 August, 2005
D. Schlör, J. Pfister, and A. Hotho. 2023 the 7th International Conference on Medical and Health Informatics (ICMHI), page 136–141. New York, NY, USA, Association for Computing Machinery, (2023)
T. Niebler, M. Becker, C. Pölitz, and A. Hotho. Proceedings of the ISWC 2017 Posters & Demonstrations and Industry Tracks co-located with 16th International Semantic Web Conference (ISWC 2017), (2017)
D. Nguyen, N. Smith, and C. Rosé. Proceedings of the 5th ACL-HLT Workshop on Language Technology for Cultural Heritage, Social Sciences, and Humanities, page 115--123. Stroudsburg, PA, USA, Association for Computational Linguistics, (2011)
X. Zhang, and Y. LeCun. (2015)cite arxiv:1502.01710Comment: This technical report is superseded by a paper entitled "Character-level Convolutional Networks for Text Classification", arXiv:1509.01626. It has considerably more experimental results and a rewritten introduction.
K. Liu, B. Fang, and W. Zhang. Proceedings of the 19th ACM International Conference on Information and Knowledge Management, page 1109--1118. New York, NY, USA, ACM, (2010)
P. Kluegl, M. Toepfer, F. Lemmerich, A. Hotho, and F. Puppe. Mathematical Methodologies in Pattern Recognition and Machine Learning Springer Proceedings in Mathematics & Statistics, (2013)