wordspace (version 0.2-0)

context.vectors: Compute Bag-of-Words Context Vectors (wordspace)

Description

Compute bag-of-words context vectors as proposed by Sch<U+00FC>tze (1998) for automatic word sense disambiguation and induction. Each context vector is the centroid of the DSM vectors of all terms occurring in the context.

Usage

context.vectors(M, contexts, split = "\\s+",
                drop.missing = TRUE, row.names=NULL)

Arguments

M

numeric matrix of row vectors for the terms specified by rownames(M), or an object of class dsm

contexts

the contexts for which bag-of-words representations are to be computed. Either a character vector, for which each item is split into a bag of terms, or a list of vectors that can be used as row indices into M. Vector representations for all terms in a bag are then looked up in M and averaged.

split

Perl regular expression determining how contexts given as a character vector are split into terms. The default behaviour is to split on whitespace.

drop.missing

if TRUE (default), contexts that do not contain any known terms are silently dropped; otherwise the corresponding context vectors will be all zeroes.

row.names

a character vector of the same length as contexts, specifying row names for the resulting matrix of centroid vectors

Value

A numeric matrix with the same number of columns as M and one row for each context (excluding contexts without known terms if drop.missing=TRUE). If the vector contexts has names or row.names is specified, the matrix rows will be labelled accordingly. Otherwise the row labels correspond to the indices of the respective entries in contexts, so matrix rows can always be identified unambiguously if drop.missing=TRUE.

Details

Bag-of-words context vectors are computed by taking the centroid of the term vectors of all known terms in each context. Neither word order nor any other structural properties of the contexts are taken into account.

References

Sch<U+00FC>tze, Hinrich (1998). Automatic word sense discrimination. Computational Linguistics, 24(1), 97--123.

See Also

SemCorWSD

Examples

# NOT RUN {
# illustration of WSD algorithm: 6 sentences each for two senses of "vessel"
VesselWSD <- subset(SemCorWSD, target == "vessel")
with(VesselWSD, cat(paste0(sense, ": ", sentence, "\n")))

# provide sense labels in case some contexts are dropped b/c of too many missing words
Centroids <- with(VesselWSD, context.vectors(DSM_Vectors, lemma, row.names=sense))
Centroids[, 1:5]

(res <- kmeans(Centroids, 2)$cluster) # flat clustering with k-means
table(rownames(Centroids), res)       # ... works perfectly

# }
# NOT RUN {
plot(hclust(dist.matrix(Centroids, as.dist=TRUE)))
# }