semnet_window {corpustools}R Documentation

Create a semantic network based on the co-occurence of tokens in token windows

Description

This function calculates the co-occurence of features and returns a network/graph in the igraph format, where nodes are tokens and edges represent the similarity/adjacency of tokens. Co-occurence is calcuated based on how often two tokens co-occurr within a given token distance.

If a featureHits object is given as input, then for for query hits that have multiple positions (i.e. terms connected with AND statements or word proximity) the raw count score is biased. For the count_* measures therefore only the first position of the query hit is used.

Usage

semnet_window(
  tc,
  feature = "token",
  measure = c("con_prob", "cosine", "count_directed", "count_undirected", "chi2"),
  context_level = c("document", "sentence"),
  window.size = 10,
  direction = "<>",
  backbone = F,
  n.batches = 5,
  matrix_mode = c("positionXwindow", "windowXwindow")
)

Arguments

tc

a tCorpus or a featureHits object (i.e. the result of search_features)

feature

The name of the feature column

measure

The similarity measure. Currently supports: "con_prob" (conditional probability), "cosine" similarity, "count_directed" (i.e number of cooccurrences) and "count_undirected" (same as count_directed, but returned as an undirected network, chi2 (chi-square score))

context_level

Determine whether features need to co-occurr within "documents" or "sentences"

window.size

The token distance within which features are considered to co-occurr

direction

Determine whether co-occurrence is assymmetricsl ("<>") or takes the order of tokens into account. If direction is '<', then the from/x feature needs to occur before the to/y feature. If direction is '>', then after.

backbone

If True, add an edge attribute for the backbone alpha

n.batches

To limit memory use the calculation is divided into batches. This parameter controls the number of batches.

matrix_mode

There are two approaches for calculating window co-occurrence (see details). By default we use positionXmatrix, but matrixXmatrix is optional because it might be favourable for some uses, and might make more sense for cosine similarity.

Details

There are two approaches for calculating window co-occurrence. One is to measure how often a feature occurs within a given token window, which can be calculating by calculating the inner product of a matrix that contains the exact position of features and a matrix that contains the occurrence window. We refer to this as the "positionXwindow" mode. Alternatively, we can measure how much the windows of features overlap, for which take the inner product of two window matrices, which we call the "windowXwindow" mode. The positionXwindow approach has the advantage of being easy to interpret (e.g. how likely is feature "Y" to occurr within 10 tokens from feature "X"?). The windowXwindow mode, on the other hand, has the interesting feature that similarity is stronger if tokens co-occurr more closely together (since then their windows overlap more), but this only works well for similarity measures that normalize the similarity (e.g., cosine). Currently, we only use the positionXwindow mode, but windowXwindow could be interesting to use as well, and for cosine it might actually make more sense.

Value

an Igraph graph in which nodes are features and edges are similarity scores

Examples

text = c('A B C', 'D E F. G H I', 'A D', 'GGG')
tc = create_tcorpus(text, doc_id = c('a','b','c','d'), split_sentences = TRUE)

g = semnet_window(tc, 'token', window.size = 1)
g
igraph::get.data.frame(g)
plot_semnet(g)

[Package corpustools version 0.4.10 Index]