r - 如何使用udpipe软件包在R中进行“单词聚类”？

我在R中使用udpipe包进行一些文本挖掘。我已经按照本教程进行操作：https://cran.r-project.org/web/packages/udpipe/vignettes/udpipe-usecase-postagging-lemmatisation.html#nouns__adjectives_used_in_same_sentence，但是现在，我有点卡住了。

实际上，我想将两个以上的单词组合在一起，以便能够识别诸如“从黄昏到黎明”之类的键表达。

因此，我想知道是否可以根据上面的图中的图形来执行一种聚类算法，以“合并”强烈且频繁的单词！连在一起？如果是，怎么办？

还有其他方法吗？

谢谢

最佳答案

根据您提供的教程，这是两个选项（使用自我网络和社区检测）。

library(udpipe)
data(brussels_reviews)
comments <- subset(brussels_reviews, language %in% "es")

ud_model <- udpipe_download_model(language = "spanish")
ud_model <- udpipe_load_model(ud_model$file_model)
x <- udpipe_annotate(ud_model, x = comments$feedback, doc_id = comments$id)
x <- as.data.frame(x)


cooc <- cooccurrence(x = subset(x, upos %in% c("NOUN", "ADJ")),
                     term = "lemma",
                     group = c("doc_id", "paragraph_id", "sentence_id"))
head(cooc)

library(igraph)
library(ggraph)
library(ggplot2)
wordnetwork <- head(cooc, 30)
wordnetwork <- graph_from_data_frame(wordnetwork)
ggraph(wordnetwork, layout = "fr") +
        geom_edge_link(aes(width = cooc, edge_alpha = cooc), edge_colour = "pink") +
        geom_node_text(aes(label = name), col = "darkgreen", size = 4) +
        theme_graph(base_family = "Arial Narrow") +
        theme(legend.position = "none") +
        labs(title = "Cooccurrences within sentence", subtitle = "Nouns & Adjective")


### Option 1: using ego-networks
V(wordnetwork) # the graph has 23 vertices
ego(wordnetwork, order = 2) # 2.0 level ego network for each vertex
ego(wordnetwork, order = 1, nodes = 10) # 1.0 level ego network for the 10th vertex (publico)


### Option 2: using community detection

# Community structure detection based on edge betweenness (http://igraph.org/r/doc/cluster_edge_betweenness.html)
cluster_edge_betweenness(wordnetwork, weights = E(wordnetwork)$cooc)

# Community detection via random walks (http://igraph.org/r/doc/cluster_walktrap.html)
cluster_walktrap(wordnetwork, weights = E(wordnetwork)$cooc, steps = 2)

# Community detection via optimization of modularity score
# This works for undirected graphs only
wordnetwork2 <- as.undirected(wordnetwork) # an undirected graph
cluster_fast_greedy(wordnetwork2, weights = E(wordnetwork2)$cooc)

# Note that you can plot community object
comm <- cluster_fast_greedy(wordnetwork2, weights = E(wordnetwork2)$cooc)
plot_dendrogram(comm)