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course:acl2010:schedule [2010/06/03 04:50]
schtepf created
course:acl2010:schedule [2018/07/26 09:35] (current)
schtepf [Schedule & handouts]
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 [[course:acl2010:start|Start page]] – [[course:acl2010:start|Start page]] –
 **Schedule** – **Schedule** –
-[[course:material|Downloads Links]] –+[[course:material|Software data sets]] –
 [[course:bibliography|Bibliography]] [[course:bibliography|Bibliography]]
  
  
 ===== Schedule & handouts ===== ===== Schedule & handouts =====
 +
 +=== Part 1 ===
 +
 +[[http://wordspace.collocations.de/lib/exe/fetch.php/course:acl2010:naacl2010_part1.slides.pdf|Presentation slides]] (PDF, 1.9 MiB) -- [[http://wordspace.collocations.de/lib/exe/fetch.php/course:acl2010:naacl2010_part1.handout.pdf|handout]] (PDF, 1.0 MiB) 
 +
 +  * **Introduction**
 +    * motivation and brief history of distributional semantics
 +    * common DSM architectures
 +    * prototypical applications
 +
 +  * **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)
 +
 +  * **Usage and evaluation of DSM**
 +    * what to do with DSM distances
 +    * attributional vs. relational similarity
 +    * evaluation tasks & results for attributional similarity 
 +
 +----
 +
 +=== Part 2 ===
 +
 +{{:icon_warn.png?32 }}
 +
 +//Part 2 was not covered in the tutorial session at NAACL-HLT 2010.  An extended version of the presentation slides & handout has been superseded by a five-part tutorial presented at [[course:esslli2018:start|ESSLLI 2016 & 2018]].//
 +\\
 +\\
 +
 +  * **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