Distributional Semantic Models in R

An interactive laboratory for research on distributional semantic models ('DSM', see < https://en.wikipedia.org/wiki/Distributional_semantics> for more information).


Version 0.2-0:

  • the first public release of the wordspace package \o/

Version 0.1-24:

  • read.dsm.triplet can now load marginal frequencies from separate files, so it is a full-fledged replacement for read.dsm.ucs (though less memory-efficient than the native UCS format for very large models)
  • RC 2 for public release

Version 0.1-23:

  • completed vignette with tutorial introduction
  • RC 1 for public release

Version 0.1-22:

  • clean up & complete documentation in preparation for first public release
  • reduce size of example data (verb-noun triples, pre-compiled DSM vectors)

Version 0.1-21:

  • replace readr::read_delim() with iotools::read.delim.raw(), which is slightly faster and leaner; also avoids expensive dependencies of readr such as BH (Boost libraries)
  • implemented work-arounds to support compressed files and different character encodings with iotools
  • package test for file input (triplet and UCS format, different encodings) with suitable sample files in extdata/

Version 0.1-20:

  • new sample data: DSM objects for small illustrative term-term and term-context matrix

Version 0.1-19:

  • complete basic documentation for all functions and data sets
  • data set DSM_VerbNounTriples_DESC removed to reduce package size
  • dsm.projection() now supports power-scaling for SVD-based projection methods

Version 0.1-18:

  • efficient truncated SVD of sparse matrix using SVDLIBC code from 'sparsesvd' package
  • faster reading of triplet files with 'readr' package (though not very memory-efficient)

Version 0.1-17:

  • Minkowski distance and length measures generalized to 0 <= p < 1 (but not homogeneous for p < 1, hence not a proper mathematical norm)

Version 0.1-16:

  • plot() method for dist.matrix for easy visualization of neighbourhood graphs
  • head() methods to extract top left corner of DSM object (dsm) or distance matrix (dist.matrix)
  • print() method for DSM objects, so users don't accidentally print a large co-occurrence matrix
  • new sample data set: DSM_Vectors with 100-dimensional pre-compiled representations for selected words
  • new sample data: typical singular values from term-context matrix
  • new sample data: "goods" example illustrating dimensionality reduction based on correlations

Version 0.1-14:

  • new evaluation task: SemCorWSD (preliminary version)
  • CITATION entry with official reference (Evert 2014)
  • enhanced functionality in nearest.neighbours(): support for cross-distance setting, targets can be given as vectors or by name, neighbour search in pre-computed distance or similarity matrix, optionally return distance matrix for target and its neighbours

Version 0.1-13:

  • Rcpp implementation of scaleMargins() further reduces memory overhead (with in-place operation for internal use)
  • as.dsm() method converts term-document and document-term matrices from tm package into DSM objects
  • added support functions for evaluation of DSMs in standard tasks (multiple choice, similarity correlation and clustering)
  • new sample data sets: tables of verb-noun cooccurrences from BNC and DESC corpora
  • new evaluation tasks: RG65, WordSim353, ESSLLI08_Nouns

Version 0.1-10:

  • use Rcpp instead of deprecated .C() native code interface
  • for performance reasons, .C() was used with DUP=FALSE, which is no longer allowed as of R 3.1.0
  • in addition, some package tests for dsm.score(), dist.matrix() and dsm.projection() were added
  • the package now depends on Rcpp (>= 0.11.0) and R (>= 3.0.0)

Version 0.1:

  • partial re-design of DSM objects and basic functions
  • handling of sparse and non-negative co-occurrence matrices has been re-thought
  • not fully compatible with v0.0 series (but basic usage should not be affected)

Version 0.0-25:

  • randomized SVD available as separate function rsvd()

Version 0.0-24

  • OpenMP no longer activated by default
  • wordspace.openmp() to check for OpenMP support and select the number of parallel threads

Version 0.0-23

  • further performance improvements
  • dist.matrix() uses less memory and is considerably faster for cosine/angle distance measure
  • new function pair.distances() computes distances or neighbour ranks for a list of word pairs efficiently
  • nearest.neighbours() automatically processes a long list of lookup terms in moderately sized batches

Version 0.0-21:

  • experimental support for OpenMP on appropriate platforms
  • n/a on Mac OS X in the default R installation (but achieves speed-up if expressly activated)
  • parallelization only used if more than 100 M operations have to be carried out (purely heuristic limit)
  • first experiments suggests that using more than 4 or 8 threads brings little benefit with enormous overhead
  • setting OMP_NUM_THREADS is strongly recommended but may also affect BLAS matrix operations (e.g. with OpenBLAS)

Reference manual

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0.2-6 by Stefan Evert, 2 years ago


Browse source code at https://github.com/cran/wordspace

Authors: Stefan Evert [http://www.stefan-evert.de/]

Documentation:   PDF Manual  

GPL-3 license

Imports Rcpp, sparsesvd, iotools, methods, stats, utils, graphics, grDevices, cluster, MASS

Depends on Matrix

Suggests knitr, rmarkdown, tm, testthat

Linking to Rcpp

Imported by SFtools, kernelPhil.

Suggested by celltrackR.

See at CRAN