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course:acl2010:start [2010/04/26 02:13]
schtepf 		course:acl2010:start [2018/07/26 09:19]
schtepf [Instructor]
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 //Tutorial at the [[http://naaclhlt2010.isi.edu/|NAACL-HLT 2010]] Conference, Los Angeles, 1 June 2010//  //Tutorial at the [[http://naaclhlt2010.isi.edu/|NAACL-HLT 2010]] Conference, Los Angeles, 1 June 2010// 
  
 +  * [[course:acl2010:schedule|Course schedule & handouts]]
 +  * [[course:material|Software & data sets]]
 +  * [[course:bibliography|Suggested readings (bibliography)]]
  
 ===== Tutorial description ===== ===== Tutorial description =====
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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. 
 + 
 +{{:icon_star.png?24 }} 
 +Updated versions of the course materials can be found at the [[:course:esslli2018:schedule|ESSLLI 2018 course]].

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