r - 应用 tm 方法时一个变量的多个结果 "stemCompletion"

Question

我有一个语料库，其中包含 3 个变量(ID、标题、摘要)的 15 个观察结果的日志数据。我使用 R Studio 从 .csv 文件中读取数据(每次观察一行)。在执行一些文本挖掘操作时，我在使用方法 stemCompletion 时遇到了一些麻烦。应用 stemCompletion 后，我观察到 .csv 的每个词干行都提供了 3 次结果。所有其他 tm 方法(例如 stemDocument)仅产生一个结果。我想知道为什么会发生这种情况以及如何解决这个问题

我使用了下面的代码:

data.corpus <- Corpus(DataframeSource(data))  
data.corpuscopy <- data.corpus
data.corpus <- tm_map(data.corpus, stemDocument)
data.corpus <- tm_map(data.corpus, stemCompletion, dictionary=data.corpuscopy) 

应用stemDocument后的单个结果是例如

"> data.corpus[[1]]

physic environ   sourc  innov investig  attribut  innov space
          investig  physic space intersect  innov  innov     relev attribut  physic space   innov        reflect  chang natur  innov  technolog advanc  servic  mean chang  argu   develop  innov space similar embodi  divers set  valu   collabor open  sustain use  literatur review interview  benchmark    examin  relationship  physic environ  innov         literatur review   interview underlin innov   communic  human centr process   result five attribut  innov space  present collabor enabl modifi smart attract   reflect       provid perspect   challeng    support innov creation  develop physic space   add   conceptu develop  innov space  outlin physic space   innov servic"

在使用 stemCompletion 之后，结果出现了 3 次:

"$`1`
physical environment source innovation investigation attributes innovation space investigation physical space intersect innovation innovation relevant attributes physical space innovation reflect changes nature innovation technological advancements service meanwhile changes argues develop innovation space similarity embodies diversified set valuable collaboration open sustainability used literature review interviews benchmarking examine relationships physical environment innovation literature review interviews underline innovation communicative human centred processes result five attributes innovation space present collaboration enablers modifiability smartness attractiveness reflect provide perspectives challenge support innovation creation develop physical space addition conceptual develop innovation space outlines physical space innovation service
physical environment source innovation investigation attributes innovation space investigation physical space intersect innovation innovation relevant attributes physical space innovation reflect changes nature innovation technological advancements service meanwhile changes argues develop innovation space similarity embodies diversified set valuable collaboration open sustainability used literature review interviews benchmarking examine relationships physical environment innovation literature review interviews underline innovation communicative human centred processes result five attributes innovation space present collaboration enablers modifiability smartness attractiveness reflect provide perspectives challenge support innovation creation develop physical space addition conceptual develop innovation space outlines physical space innovation service
physical environment source innovation investigation attributes innovation space investigation physical space intersect innovation innovation relevant attributes physical space innovation reflect changes nature innovation technological advancements service meanwhile changes argues develop innovation space similarity embodies diversified set valuable collaboration open sustainability used literature review interviews benchmarking examine relationships physical environment innovation literature review interviews underline innovation communicative human centred processes result five attributes innovation space present collaboration enablers modifiability smartness attractiveness reflect provide perspectives challenge support innovation creation develop physical space addition conceptual develop innovation space outlines physical space innovation service"

下面是一个可重现的例子:

包含三个变量的三个观察值的 .csv 文件:

ID;Text A;Text B
1;Below is the first title;Innovation and Knowledge Management
2;And now the second Title;Organizational Performance and Learning are very important
3;The third title;Knowledge plays an important rule in organizations

下面是我用过的词干提取方法

data = read.csv2("Test.csv")
data[,2]=as.character(data[,2])
data[,3]=as.character(data[,3])

corpus <- Corpus(DataframeSource(data)) 
corpuscopy <- corpus
corpus <- tm_map(corpus, stemDocument)
corpus[[1]]

corpus <- tm_map(corpus, stemCompletion, dictionary=corpuscopy)
inspect(corpus[1:3])

在我看来，这取决于 .csv 中使用的变量数量，但我不知道为什么。

Answer 1

stemCompletion 函数似乎有些奇怪。在 tm 0.6 版本中如何使用 stemCompletion 并不明显。有一个很好的解决方法 here我已将其用于此答案。

首先，制作您拥有的 CSV 文件:

dat <- read.csv2( text = 
                  "ID;Text A;Text B
1;Below is the first title;Innovation and Knowledge Management
2;And now the second Title;Organizational Performance and Learning are very important
3;The third title;Knowledge plays an important rule in organizations")

write.csv2(dat, "Test.csv", row.names = FALSE)

读入，转换为语料库，并提取单词:

data = read.csv2("Test.csv")
data[,2]=as.character(data[,2])
data[,3]=as.character(data[,3])

corpus <- Corpus(DataframeSource(data)) 
corpuscopy <- corpus
library(SnowballC)
corpus <- tm_map(corpus, stemDocument)

看看它是否有效:

inspect(corpus)

<<VCorpus (documents: 3, metadata (corpus/indexed): 0/0)>>

[[1]]
<<PlainTextDocument (metadata: 7)>>
1
Below is the first titl
Innovat and Knowledg Manag

[[2]]
<<PlainTextDocument (metadata: 7)>>
2
And now the second Titl
Organiz Perform and Learn are veri import

[[3]]
<<PlainTextDocument (metadata: 7)>>
3
The third titl
Knowledg play an import rule in organ

这是让 stemCompletion 工作的好方法:

stemCompletion_mod <- function(x,dict=corpuscopy) {
  PlainTextDocument(stripWhitespace(paste(stemCompletion(unlist(strsplit(as.character(x)," ")),dictionary=dict, type="shortest"),sep="", collapse=" ")))
}

检查输出以查看词干是否已完成:

lapply(corpus, stemCompletion_mod)

[[1]]
<<PlainTextDocument (metadata: 7)>>
1 Below is the first title Innovation and Knowledge Management

[[2]]
<<PlainTextDocument (metadata: 7)>>
2 And now the second Title Organizational Performance and Learning are NA important

[[3]]
<<PlainTextDocument (metadata: 7)>>
3 The third title Knowledge plays an important rule in organizations

成功!