Intro to word stemming and stem completion

Still, another useful preprocessing step involves word-stemming and stem completion. Word stemming reduces words to unify across documents. For example, the stem of "computational", "computers" and "computation" is "comput". But because "comput" isn't a real word, we want to reconstruct the words so that "computational", "computers", and "computation" all refer to a recognizable word, such as "computer". The reconstruction step is called stem completion.

The tm package provides the stemDocument() function to get to a word's root. This function either takes in a character vector and returns a character vector, or takes in a PlainTextDocument and returns a PlainTextDocument.

For example,

stemDocument(c("computational", "computers", "computation"))

returns "comput" "comput" "comput".

You will use stemCompletion() to reconstruct these word roots back into a known term. stemCompletion() accepts a character vector and a completion dictionary. The completion dictionary can be a character vector or a Corpus object. Either way, the completion dictionary for our example would need to contain the word "computer," so all instances of "comput" can be reconstructed.

이 연습은 강의의 일부입니다

Text Mining with Bag-of-Words in R

강의 보기

연습 안내

Create a vector called complicate consisting of the words "complicated", "complication", and "complicatedly" in that order.
Store the stemmed version of complicate to an object called stem_doc.
Create comp_dict that contains one word, "complicate".
Create complete_text by applying stemCompletion() to stem_doc. Re-complete the words using comp_dict as the reference corpus.
Print complete_text to the console.

실습형 인터랙티브 연습

이 예제를 이 샘플 코드를 완성하여 풀어보세요.

# Create complicate
complicate <- ___

# Perform word stemming: stem_doc
stem_doc <- ___

# Create the completion dictionary: comp_dict
comp_dict <- ___

# Perform stem completion: complete_text 
complete_text <- ___

# Print complete_text
complete_text

코드 편집 및 실행