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	An implementation of all methods presented in the tutorial will be made available on this Web site, based on the open-source statistical programming language [[http://www.r-project.org/|R]].  With its sophisticated visualisation and data analysis features and an enormous choice of add-on packages, R provides an excellent "toy laboratory" for DSM research and is even powerful enough for mid-sized applications.		An implementation of all methods presented in the tutorial will be made available on this Web site, based on the open-source statistical programming language [[http://www.r-project.org/|R]].  With its sophisticated visualisation and data analysis features and an enormous choice of add-on packages, R provides an excellent "toy laboratory" for DSM research and is even powerful enough for mid-sized applications.
-	===== Schedule =====	+	===== Instructor =====
-	- **Introduction**	+	This tutorial was taught by [[http://purl.org/stefan.evert|Stefan Evert]] (University of Osnabrück, Germany). Don't hesitate to contact me at [[stefan.evert@uos.de]] if you have any questions.
-	* motivation and brief history of distributional semantics	+
-	* common DSM architectures	+
-	* prototypical applications	+
-	* concrete examples used in the tutorial	+
-	- **Taxonomy of DSM parameters** including	+
-	* size and type of context window	+
-	* feature scaling (tf.idf, statistical association measures, ...)	+
-	* normalisation and standardisation of rows and/or columns	+
-	* distance/similarity measures: Euclidean, Minkowski p-norms, cosine, entropy-based, ...	+
-	* dimensionality reduction: feature selection, SVD, random indexing (RI)	+
-	- **Elements of matrix algebra** for DSM	+
-	* basic matrix and vector operations	+
-	* norms and distances, angles, orthogonality	+
-	* projection and dimensionality reduction	+
-	- **Making sense of DSMs**: mathematical analysis and visualisation techniques	+
-	* nearest neighbours and clustering	+
-	* semantic maps: PCA, MDS, SOM	+
-	* visualisation of high-dimensional spaces	+
-	* supervised classification based on DSM vectors	+
-	* understanding dimensionality reduction with SVD and RI	+
-	* term-term vs. term-context matrix, connection to first-order association	+
-	* SVD as a latent class model	+
-	- **Current research topics** and future directions	+
-	* overview of current research on DSMs	+
-	* evaluation tasks and data sets	+
-	* available "off-the-shelf" DSM software	+
-	* limitations and key problems of DSMs	+
-	* trends for future work	+
-	Each of the five parts will be compressed into a slot of roughly 30 minutes.	+	The tutorial is based on joint work with Alessandro Lenci and Marco Baroni.
-		+
-		+
-	===== Contact =====	+
-		+
-	This tutorial will be taught by [[http://purl.org/stefan.evert|Stefan Evert]] (University of Osnabrück, Germany).  Don't hesitate to contact me at [[stefan.evert@uos.de]] if you have any questions.	+