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course:acl2010:start [2010/04/26 02:08]
schtepf |
course:acl2010:start [2018/07/26 09:20] (current)
schtepf [Instructor] |
| //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// |
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| | * [[course:acl2010:schedule|Course schedule & handouts]] |
| | * [[course:material|Software & data sets]] |
| | * [[course:bibliography|Suggested readings (bibliography)]] |
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| ===== Course description ===== | ===== Tutorial description ===== |
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| Distributional semantic models (DSM) -- also known as "word space" or "distributional similarity" models -- are based on the assumption that the meaning of a word can (at least to a certain extent) be inferred from its usage, i.e. its distribution in text. Therefore, these models build high-dimensional vector representations through a statistical analysis of the contexts in which words occur. | Distributional semantic models (DSM) -- also known as "word space" or "distributional similarity" models -- are based on the assumption that the meaning of a word can (at least to a certain extent) be inferred from its usage, i.e. its distribution in text. Therefore, these models build high-dimensional vector representations through a statistical analysis of the contexts in which words occur. |
| 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. |
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| | ===== Instructor ===== |
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| | 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. |
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| | The tutorial is based on joint work with Alessandro Lenci and Marco Baroni. |
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| | {{:icon_star.png?24 }} |
| | Updated versions of the course materials can be found in the [[:course:esslli2018:schedule|ESSLLI 2018 course]]. |
