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course:material [2018/07/26 11:48]
schtepf
course:material [2019/05/17 09:49]
schtepf [Software for the course]
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   - Use the installer built into RStudio (or the standard R GUI) to install the following packages from the CRAN archive:    - Use the installer built into RStudio (or the standard R GUI) to install the following packages from the CRAN archive: 
     * ''sparsesvd''     * ''sparsesvd''
-    * ''iotools'' +    * ''wordspace'' 
-    * ''tm'' (optional) +    * optional: ''tm''''quanteda''''Rtsne''''uwot'', ''wordcloud'', ''shiny'', ''corpustools'', ''spacyr'', ''udpipe'' (don't worry if some of these fail to install)
-    * ''quanteda'' (optional) +
-    * ''Rcpp'' (needed on Linux only) +
-  - Install the ''wordspace'' package itself.  It is available from CRAN through the standard installerbut you may be asked to use the latest version available here: +
-    * ''wordspace'' v0.2-0: [[http://wordspace.r-forge.r-project.org/downloads/wordspace_0.2-0.tar.gz|Source/Linux]] – [[http://wordspace.r-forge.r-project.org/downloads/wordspace_0.2-0.tgz|MacOS]] – [[http://wordspace.r-forge.r-project.org/downloads/wordspace_0.2-0.zip|Windows]] +
-    * download a suitable version of the package for your platform +
-    * in the RStudio installer, select “Install from: Package Archive File”+
   - During the course, you will be asked to install a further package with additional evaluation tasks (''wordspaceEval'') from a password-protected Web page:   - During the course, you will be asked to install a further package with additional evaluation tasks (''wordspaceEval'') from a password-protected Web page:
     * ''wordspaceEval'' v0.1: [[http://www.collocations.de/data/protected/wordspaceEval_0.1.tar.gz|Source/Linux]] – [[http://www.collocations.de/data/protected/wordspaceEval_0.1.tgz|MacOS]] – [[http://www.collocations.de/data/protected/wordspaceEval_0.1.zip|Windows]] (login required)     * ''wordspaceEval'' v0.1: [[http://www.collocations.de/data/protected/wordspaceEval_0.1.tar.gz|Source/Linux]] – [[http://www.collocations.de/data/protected/wordspaceEval_0.1.tgz|MacOS]] – [[http://www.collocations.de/data/protected/wordspaceEval_0.1.zip|Windows]] (login required)
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   - Download the sample data files listed below   - Download the sample data files listed below
   - Download one or more of the pre-compiled DSMs listed below   - Download one or more of the pre-compiled DSMs listed below
 +
 +==== Getting the latest & greatest ====
 +
 +During the course, you may be asked to install a new version of ''wordspace'' that hasn't been submitted to CRAN yet.  In this case, please follow these instructions:
 +
 +  - Use the installer built into RStudio (or the standard R GUI) to install the following packages from the CRAN archive: 
 +    * ''sparsesvd''
 +    * ''iotools''
 +    * ''Rcpp'' (needed on Linux only)
 +  - Download an appropriate version of the package for your platform:
 +    * ''wordspace'' v0.2-0: [[http://wordspace.r-forge.r-project.org/downloads/wordspace_0.2-0.tar.gz|Source/Linux]] – [[http://wordspace.r-forge.r-project.org/downloads/wordspace_0.2-0.tgz|MacOS]] – [[http://wordspace.r-forge.r-project.org/downloads/wordspace_0.2-0.zip|Windows]]
 +  - In the RStudio installer, select “Install from: Package Archive File”
 +
 +You can also check the [[http://wordspace.r-forge.r-project.org/|wordspace homepage]] for new releases and installation instructions.
  
 ===== Example data sets ===== ===== Example data sets =====
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 ===== Pre-compiled DSMs ===== ===== Pre-compiled DSMs =====
  
-Pre-compiled DSMs for use with the ''wordspace'' package for R. Each model is contained in an ''.rda'' file, and can be loaded into R with the command ''load("model.rda")''.+Pre-compiled DSMs for use with the ''wordspace'' package for R. Each model is contained in an ''.rda'' file, which can be loaded into R with the command ''load("model.rda")'' and creates an object with the same name (''model'').
  
 ==== DSMs based on the English Wikipedia ==== ==== DSMs based on the English Wikipedia ====
  
-These models were compiled from ''WP500'', a 200-million word subset of the Wackypedia corpus comprising the first 500 words of each article. Each model covers a vocabulary of the 50,000 most frequent content words (lemmatized) in the corpus and has at least 50,000 feature dimensions.+These models were compiled from ''WP500'', a 200-million word subset of the Wackypedia corpus comprising the first 500 words of each article. Each model covers a vocabulary of the 50,000 most frequent content words (lemmatized) in the corpus and has at least 50,000 feature dimensions. The latent SVD dimensions are based on log-transformed sparse simple-ll scores with L2-normalization. Power scaling with Caron $P = 0$ (i.e. equalization of the latent dimensions) has been applied, but the reduced vectors are not re-normalized
  
-  * dependency-filtered: ''[[http://www.collocations.de/data/WP500_DepFilter_Lemma.rda|WP500_DepFilter_Lemma.rda]]'' (30.MB) – 500 latent SVD dimensions: ''[[http://www.collocations.de/data/WP500_DepFilter_Lemma_svd500.rda|WP500_DepFilter_Lemma_svd500.rda]]'' (175.MB) +  * dependency-filtered: ''[[http://corpora.linguistik.uni-erlangen.de/data/wordspace/WP500_DepFilter_Lemma.rda|WP500_DepFilter_Lemma.rda]]'' (31.MB) – 500 latent SVD dimensions: ''[[http://corpora.linguistik.uni-erlangen.de/data/wordspace/WP500_DepFilter_Lemma_svd500.rda|WP500_DepFilter_Lemma_svd500.rda]]'' (179.MB) 
-  * dependency-structured: ''[[http://www.collocations.de/data/WP500_DepStruct_Lemma.rda|WP500_DepStruct_Lemma.rda]]'' (30.MB) – 500 latent SVD dimensions: ''[[http://www.collocations.de/data/WP500_DepStruct_Lemma_svd500.rda|WP500_DepStruct_Lemma_svd500.rda]]'' (176.MB) +  * dependency-structured: ''[[http://corpora.linguistik.uni-erlangen.de/data/wordspace/WP500_DepStruct_Lemma.rda|WP500_DepStruct_Lemma.rda]]'' (31.MB) – 500 latent SVD dimensions: ''[[http://corpora.linguistik.uni-erlangen.de/data/wordspace/WP500_DepStruct_Lemma_svd500.rda|WP500_DepStruct_Lemma_svd500.rda]]'' (180.MB) 
-  * L2/R2 surface span: ''[[http://www.collocations.de/data/WP500_Win2_Lemma.rda|WP500_Win2_Lemma.rda]]'' (50.MB) – 500 latent SVD dimensions: ''[[http://www.collocations.de/data/WP500_Win2_Lemma_svd500.rda|WP500_Win2_Lemma_svd500.rda]]'' (173.MB) +  * L2/R2 surface span: ''[[http://corpora.linguistik.uni-erlangen.de/data/wordspace/WP500_Win2_Lemma.rda|WP500_Win2_Lemma.rda]]'' (51.MB) – 500 latent SVD dimensions: ''[[http://corpora.linguistik.uni-erlangen.de/data/wordspace/WP500_Win2_Lemma_svd500.rda|WP500_Win2_Lemma_svd500.rda]]'' (177.MB) 
-  * L5/R5 surface span: ''[[http://www.collocations.de/data/WP500_Win5_Lemma.rda|WP500_Win5_Lemma.rda]]'' (99.MB) – 500 latent SVD dimensions: ''[[http://www.collocations.de/data/WP500_Win5_Lemma_svd500.rda|WP500_Win5_Lemma_svd500.rda]]'' (176.MB) +  * L5/R5 surface span: ''[[http://corpora.linguistik.uni-erlangen.de/data/wordspace/WP500_Win5_Lemma.rda|WP500_Win5_Lemma.rda]]'' (103.MB) – 500 latent SVD dimensions: ''[[http://corpora.linguistik.uni-erlangen.de/data/wordspace/WP500_Win5_Lemma_svd500.rda|WP500_Win5_Lemma_svd500.rda]]'' (179.MB) 
-  * L30/R30 surface span: ''[[http://www.collocations.de/data/WP500_Win30_Lemma.rda|WP500_Win30_Lemma.rda]]'' (295.MB) – 500 latent SVD dimensions: ''[[http://www.collocations.de/data/WP500_Win30_Lemma_svd500.rda|WP500_Win30_Lemma_svd500.rda]]'' (179.MB) +  * L30/R30 surface span: ''[[http://corpora.linguistik.uni-erlangen.de/data/wordspace/WP500_Win30_Lemma.rda|WP500_Win30_Lemma.rda]]'' (311.MB) – 500 latent SVD dimensions: ''[[http://corpora.linguistik.uni-erlangen.de/data/wordspace/WP500_Win30_Lemma_svd500.rda|WP500_Win30_Lemma_svd500.rda]]'' (182.MB) 
-  * term-document model: ''[[http://www.collocations.de/data/WP500_TermDoc_Lemma.rda|WP500_TermDoc_Lemma.rda]]'' (101.MB) – 500 latent SVD dimensions: ''[[http://www.collocations.de/data/WP500_TermDoc_Lemma_svd500.rda|WP500_TermDoc_Lemma_svd500.rda]]'' (158.MB) +  * term-document model: ''[[http://corpora.linguistik.uni-erlangen.de/data/wordspace/WP500_TermDoc_Lemma.rda|WP500_TermDoc_Lemma.rda]]'' (105.MB) – 500 latent SVD dimensions: ''[[http://corpora.linguistik.uni-erlangen.de/data/wordspace/WP500_TermDoc_Lemma_svd500.rda|WP500_TermDoc_Lemma_svd500.rda]]'' (162.MB) 
-  * type contexts (L1+R1): ''[[http://www.collocations.de/data/WP500_Ctype_L1R1_Lemma.rda|WP500_Ctype_L1R1_Lemma.rda]]'' (55.MB) – 500 latent SVD dimensions: ''[[http://www.collocations.de/data/WP500_Ctype_L1R1_Lemma_svd500.rda|WP500_Ctype_L1R1_Lemma_svd500.rda]]'' (153.MB) +  * type contexts (L1+R1): ''[[http://corpora.linguistik.uni-erlangen.de/data/wordspace/WP500_Ctype_L1R1_Lemma.rda|WP500_Ctype_L1R1_Lemma.rda]]'' (55.MB) – 500 latent SVD dimensions: ''[[http://corpora.linguistik.uni-erlangen.de/data/wordspace/WP500_Ctype_L1R1_Lemma_svd500.rda|WP500_Ctype_L1R1_Lemma_svd500.rda]]'' (157.0 MB) 
-  * type contexts (L2+R2 POS tags): ''[[http://www.collocations.de/data/WP500_Ctype_L2R2pos_Lemma.rda|WP500_Ctype_L2R2pos_Lemma.rda]]'' (55.1 MB) – 500 latent SVD dimensions: ''[[http://www.collocations.de/data/WP500_Ctype_L2R2pos_Lemma_svd500.rda|WP500_Ctype_L2R2pos_Lemma_svd500.rda]]'' (172.MB) +  * type contexts (L2+R2): ''[[http://corpora.linguistik.uni-erlangen.de/data/wordspace/WP500_Ctype_L2R2_Lemma.rda|WP500_Ctype_L2R2_Lemma.rda]]'' (33.1 MB) – 500 latent SVD dimensions: ''[[http://corpora.linguistik.uni-erlangen.de/data/wordspace/WP500_Ctype_L2R2_Lemma_svd500.rda|WP500_Ctype_L2R2_Lemma_svd500.rda]]'' (64.3 MB) 
-  * word forms L2/R2: ''[[http://www.collocations.de/data/WP500_Win2_Word.rda|WP500_Win2_Word.rda]]'' (61.MB) – 500 latent SVD dimensions: ''[[http://www.collocations.de/data/WP500_Win2_Word_svd500.rda|WP500_Win2_Word_svd500.rda]]'' (182.MB) +  * type contexts (L2+R2 POS tags): ''[[http://corpora.linguistik.uni-erlangen.de/data/wordspace/WP500_Ctype_L2R2pos_Lemma.rda|WP500_Ctype_L2R2pos_Lemma.rda]]'' (56.1 MB) – 500 latent SVD dimensions: ''[[http://corpora.linguistik.uni-erlangen.de/data/wordspace/WP500_Ctype_L2R2pos_Lemma_svd500.rda|WP500_Ctype_L2R2pos_Lemma_svd500.rda]]'' (175.MB) 
-  * word forms L2/R2 with non-lemmatized features: ''[[http://www.collocations.de/data/WP500_Win2_Word_WF.rda|WP500_Win2_Word_WF.rda]]'' (65.9 MB) – 500 latent SVD dimensions: ''[[http://www.collocations.de/data/WP500_Win2_Word_WF_svd500.rda|WP500_Win2_Word_WF_svd500.rda]]'' (182.MB)+  * word forms L2/R2: ''[[http://corpora.linguistik.uni-erlangen.de/data/wordspace/WP500_Win2_Word.rda|WP500_Win2_Word.rda]]'' (63.MB) – 500 latent SVD dimensions: ''[[http://corpora.linguistik.uni-erlangen.de/data/wordspace/WP500_Win2_Word_svd500.rda|WP500_Win2_Word_svd500.rda]]'' (185.MB) 
 +  * word forms L2/R2 with non-lemmatized features: ''[[http://corpora.linguistik.uni-erlangen.de/data/wordspace/WP500_Win2_Word_WF.rda|WP500_Win2_Word_WF.rda]]'' (68.9 MB) – 500 latent SVD dimensions: ''[[http://corpora.linguistik.uni-erlangen.de/data/wordspace/WP500_Win2_Word_WF_svd500.rda|WP500_Win2_Word_WF_svd500.rda]]'' (185.MB)
  
 ==== Neural word embeddings ==== ==== Neural word embeddings ====
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 Some publicly available pre-trained neural embeddings, converted into ''.rda'' format for use with the ''wordspace'' package. Some publicly available pre-trained neural embeddings, converted into ''.rda'' format for use with the ''wordspace'' package.
  
-  * word2vec: ''[[http://www.collocations.de/data/GoogleNews300_wf200k.rda|GoogleNews300_wf200k.rda]]'' (129.2 MiB) +  * word2vec: ''[[http://corpora.linguistik.uni-erlangen.de/data/wordspace/GoogleNews300_wf200k.rda|GoogleNews300_wf200k.rda]]'' (129.2 MiB) 
  
 ===== Web interfaces ===== ===== Web interfaces =====
  
-  * Web interface for several pre-trained [[http://clic.cimec.unitn.it/infomap-query/|Infomap models]] (CIMeC, U Trento)+  * Web interface for several pre-trained **[[http://clic.cimec.unitn.it/infomap-query/|Infomap models]]** (CIMeC, U Trento
 +  * Explore **[[https://corpora.linguistik.uni-erlangen.de/shiny/wordspace/word2vec/|word2vec embeddings]]** (FAU Erlangen-Nürnberg) 
 +  * Explore **[[https://corpora.linguistik.uni-erlangen.de/shiny/wordspace/WP500/|DSMs based on Wikipedia]]** (FAU Erlangen-Nürnberg)