如何查看映射到特定词干单词的原始单词

Question

我正在使用 R 中的 tm_map 进行一些文本分析。我运行以下代码（没有错误）来生成（词干和其他预处理）单词的文档术语矩阵。

  corpus = Corpus(VectorSource(textVector))
  corpus = tm_map(corpus, tolower)
  corpus = tm_map(corpus, PlainTextDocument) 
  corpus = tm_map(corpus, removePunctuation)
  corpus = tm_map(corpus, removeWords, c(stopwords("english")))
  corpus = tm_map(corpus, stemDocument, language="english")

  dtm = DocumentTermMatrix(corpus)
  mostFreqTerms = findFreqTerms(dtm, lowfreq=125) 

但是，当我查看我的（词干）mostFreqTerms 时，我发现有几个词让我思考：“嗯，是哪些词被词干产生的？” 另外，可能有一些词干词乍一看对我来说有意义，但也许我忽略了一个事实，即它们实际上包含具有不同含义的词。

我想应用此 SO 答案中描述的策略/技术来在词干提取期间保留特定术语（例如，保持“自然”和“自然化”不成为相同的词干术语。 使用 tm-package 进行文本挖掘 - 词干提取

但为了最全面地做到这一点，我希望看到映射到我最常见的词干词的所有单独单词的列表。有没有办法找到词干后产生我的mostFreqTerms 列表的单词？

编辑：可重现的示例

textVector = c("Trisha Takinawa: Here comes Mayor Adam West 
               himself. Mr. West do you have any words 
               for our viewers?Mayor Adam West: Box toaster
               aluminum maple syrup... no I take that one 
               back. Im gonna hold onto that one. 
               Now MaxPower is adding adamant
               so this example works")

      corpus = Corpus(VectorSource(textVector))
      corpus = tm_map(corpus, tolower)
      corpus = tm_map(corpus, PlainTextDocument) 
      corpus = tm_map(corpus, removePunctuation)
      corpus = tm_map(corpus, removeWords, c(stopwords("english")))
      corpus = tm_map(corpus, stemDocument, language="english")

      dtm = DocumentTermMatrix(corpus)
      mostFreqTerms = findFreqTerms(dtm, lowfreq=2) 
      mostFreqTerms

...上面的mostFreqTerms输出

[1]《亚当》《一》《西》

我正在寻找一种编程方法来确定词干词“adam”来自原始词“adam”和“adamant”。

Answer 1

在这里，您可以看到词干词“west”来自词“west”、“west”和“wester”。

import nltk

from nltk.tokenize import word_tokenize
from nltk.corpus import stopwords
from nltk.stem import RSLPStemmer
import string 

st = RSLPStemmer()
punctuations = list(string.punctuation)
textVector = "Trisha Takinawa: Here comes Mayor adams West himself. Mr. \
            West do you have any words for our viewers?Mayor Adam Wester: \
    Box toaster aluminum maple syrup... no I take that one back. Im gonna hold \
    onto that one. Now MaxPower is adding adamant so this example works"

tokens = word_tokenize(textVector.lower())
tokens = [w for w in tokens if not w in punctuations]
filtered_words = [w for w in tokens if not w in stopwords.words('english')]
steammed_words = [st.stem(w) for w in filtered_words ]

allWordDist = nltk.FreqDist(w for w in steammed_words)

for w in allWordDist.most_common(2):
    for i in range(len(steammed_words)):
        if steammed_words[i] == w[0]:
            print str(w[0])+"="+ filtered_words[i]

西=西

西=西

西=西方

广告=亚当斯

广告=亚当