====== Distributional Semantic Models (NAACL-HLT 2010)  ======

[[course:acl2010:start|Start page]] –
**Schedule** –
[[course:material|Software & data sets]] –
[[course:bibliography|Bibliography]]


===== 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 

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=== Part 2 ===

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//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  An extended version of the presentation slides & handout has been superseded by a five-part tutorial presented at [[course:esslli2018:start|ESSLLI 2016 & 2018]].//
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  * **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
