Differences
This shows you the differences between two versions of the page.
Both sides previous revision Previous revision Next revision | Previous revision | ||
course:acl2010:start [2010/04/26 02:16] schtepf |
course:acl2010:start [2018/07/26 09:20] (current) schtepf [Instructor] |
||
---|---|---|---|
Line 6: | Line 6: | ||
//Tutorial at the [[http:// | //Tutorial at the [[http:// | ||
+ | * [[course: | ||
+ | * [[course: | ||
+ | * [[course: | ||
===== Tutorial description ===== | ===== Tutorial description ===== | ||
Line 21: | Line 24: | ||
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:// | 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:// | ||
- | ===== Schedule | + | ===== Instructor |
- | - **Introduction** | + | This tutorial |
- | * 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 | + | |
- | - **Elements | + | |
- | * 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 " | + | |
- | * 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 ===== | + | Updated versions of the course materials can be found in the [[:course: |
- | + | ||
- | This tutorial will be taught by [[http:// | + |