====== Distributional Semantics – A Practical Introduction (ESSLLI 2016 & 2018)  ======

[[course:esslli2018:start|Start page]] –
**Schedule** –
[[course:material|Software & data sets]] –
[[course:bibliography|Bibliography]]

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  * extended version of part 2 practice: //input formats// (26.05.2019)
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===== Schedule & handouts =====

=== Day 1: Introduction ===

{{:course:esslli2018:dsm_tutorial_part1.slides.pdf|Presentation slides}} (PDF, 1.1 MB) – {{:course:esslli2018:dsm_tutorial_part1.handout.pdf|handout}} (PDF, 0.9 MB) – {{:course:esslli2018:part1_examples.R|R code}}

  * motivation and brief history of distributional semantics
  * common DSM architectures & prototypical applications
  * first practical exercises with the ''wordspace'' package


=== Day 2: The parameters of a DSM ===

{{:course:esslli2018:dsm_tutorial_part2.slides.pdf|Presentation slides}} (PDF, 1.3 MB) – {{:course:esslli2018:dsm_tutorial_part2.handout.pdf|handout}} (PDF, 1.0 MB) – {{:course:esslli2018:part2_examples.R|R code}} – {{:course:esslli2018:part2_input_formats.R|practice: input formats}} – {{:course:esslli2018:part2_exercise.R|exercise (DSM parameters)}}


  * taxonomy of DSM parameters: context representation, feature scaling, normalization and standardization, distance/similarity measures, dimensionality reduction
  * overview of common parameter settings & best-practice recommendations
  * practical exercises: building DSMs and exploring their parameters


=== Day 3: Applications and evaluation ===

{{:course:esslli2018:dsm_tutorial_part3.slides.pdf|Presentation slides}} (PDF, 2.0 MB) – {{:course:esslli2018:dsm_tutorial_part3.handout.pdf|handout}} (PDF, 1.8 MB) – {{:course:esslli2018:part3_examples.R|R code}} – {{:course:esslli2018:part3_exercise.R|exercise (evaluation)}}

  * attributional and relational similarity, clustering and semantic categorization, multiple-choice tasks
/*  * supervised & unsupervised classification based on DSM vectors */
  * insights from recent parameter evaluation studies
  * practical exercises: implementation and evaluation of selected tasks


=== Day 4: Elements of matrix algebra ===

{{:course:esslli2018:dsm_tutorial_part4.slides.pdf|Presentation slides}} (PDF, 0.7 MB) – {{:course:esslli2018:dsm_tutorial_part4.handout.pdf|handout}} (PDF, 0.6 MB) – {{:course:esslli2018:part4_examples.R|R code}} – {{:course:esslli2018:schuetze1998.R|bonus practice: Schütze-style WSD}} – {{:course:esslli2018:part4_exercise.R|exercise (roll your own DSM)}}

  * basic matrix and vector operations, orthogonal projection & dimensionality reduction
  * singular value decomposition (SVD)
  * practical exercises: roll your own DSM with matrix operations


=== Day 5: Making sense of DSMs ===

  * mathematical properties of and relations between different types of DSM
  * singular value decomposition (SVD) as a latent class model
  * comparison with neural vector embeddings


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