如何在 R 中找到单词和单词列表之间的语义相似度？

Question

I was attempting to find the semantic similarity between a single word and each word in a list that I have generated online using the English lexicon project. Here is the list of words that I was using. I converted to string to use the word2vec() function to train upon it later on. There are 20 words in this list.

library(word2vec)
library(readr)
library(NLP)

word.list<- read.delim("wordlist.txt", header = FALSE) 
str(word.list)
'data.frame':   1 obs. of  1 variable:
 $ V1: chr "postage enzyme textbook requirement installation eyeglasses numerical priesthood fence assemble extract domino "| __truncated__

    word.list <- structure(list(V1 = "postage", V2 = "enzyme", V3 = "textbook", V4 = "requirement", V5 = "installation", V6 = "eyeglasses", V7 = "numerical", V8 = "priesthood", V9 = "fence", V10 = "assemble", V11 = "extract", V12 = "domino", V13 = "square", V14 = "deduction", V15 = "predecessor", V16 = "liaison", V17 = "launder", V18 = "canteen", V19 = "cashier", V20 = "informal"), class = "data.frame", row.names = c(NA, -1L))

word.list.ch<- as.String(word.list)
 chr "postage enzyme textbook requirement installation eyeglasses numerical priesthood fence assemble extract domino "| __truncated__

Following an example online ( http://www.bnosac.be/index.php/blog/100-word2vec-in-r ), I have tried to train these words into a model.

model<- word2vec(word.list.ch, type = "cbow", dim = 20, iter = 20)

However, it says that the file or directory did not exist.

Training failed: fileMapper: postage enzyme textbook requirement installation eyeglasses numerical priesthood fence assemble extract domino square deduction predecessor liaison launder canteen cashier informal - No such file or directory

Could I ask why is this the case, and if there is an alternative solution?

Many thanks in advance!

data (pasted from comment)

structure(list(V1 = "postage", V2 = "enzyme", V3 = "textbook", V4 = "requirement", V5 = "installation", V6 = "eyeglasses", V7 = "numerical", V8 = "priesthood", V9 = "fence", V10 = "assemble", V11 = "extract", V12 = "domino", V13 = "square", V14 = "deduction", V15 = "predecessor", V16 = "liaison", V17 = "launder", V18 = "canteen", V19 = "cashier", V20 = "informal"), class = "data.frame", row.names = c(NA, -1L))

Answer 1

You need to train your word2vec model on a character vector of length > 1.

Training on just 20 words does not make sense, make sure you have a large corpus to train your word2vec model upon.